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In wireless communication, the channel function estimated commonly has errors due to the influence of noise, so traditional channel estimation methods cannot accurately estimate the real channel function. Aiming at this problem, we propose a channel estimation method that combines sounding reference signal (SRS) remapping with the deep-learning network BRD_ESRNet. BRD_ESRNet consists of image denoising using a deep convolutional neural network with batch renormalization (BRDNet) and an expanded superresolution convolutional neural network (ESRCNN). At the transmitter side, we first map the SRS into four-box structures, and then, the four-box structures are scattered distribution throughout the time-frequency resource block. At the receiver side, we first perform the modified least squares (LS) estimation based on the four-box structure and place the result into the top-left resource unit of the four box. Then, we perform linear interpolation for the whole resource block. Finally, we equate the estimated channel matrix to a low-resolution image containing noise and input it to BRD_ESRNet. Thus, we obtain data with high resolution and achieve the purpose of reducing the estimation error of the channel function. The experimental results show that the proposed method in this paper has a significant improvement in performance compared to the methods of Soltani et al. and Nithya et al. In this paper, the methods of Soltani et al. and Nithya et al. are referred to as methods 1 and 2, respectively. [ABSTRACT FROM AUTHOR]

This paper discusses the challenges of the hyperparameter tuning in deep learning models and proposes a green approach to the neural architecture search process that minimizes its environmental impact. The traditional approach of neural architecture search involves sweeping the entire space of possible architectures, which is computationally expensive and time-consuming. Recently, to address this issue, performance predictors have been proposed to estimate the performance of different architectures, thereby reducing the search space and speeding up the exploration process. The proposed approach aims to develop a performance predictor by training only a small percentage of the possible hyperparameter configurations. The suggested predictor can be queried to find the best configurations without training them on the dataset. Numerical examples of image denoising and classification enable us to evaluate the performance of the proposed approach in terms of performance and time complexity. [ABSTRACT FROM AUTHOR]

This paper realizes infrared image denoising, recognition, and semantic segmentation for complex electrical equipment and proposes a thermal fault diagnosis method that incorporates temperature differences. We introduce a deformable convolution module into the Denoising Convolutional Neural Network (DeDn-CNN) and propose an image denoising algorithm based on this improved network. By replacing Gaussian wrap-around filtering with anisotropic diffusion filtering, we suggest an image enhancement algorithm that employs Weighted Guided Filtering (WGF) with an enhanced Single-Scale Retinex (Ani-SSR) technique to prevent strong edge halos. Furthermore, we propose a refined detection algorithm for electrical equipment that builds upon an improved RetinaNet. This algorithm incorporates a rotating rectangular frame and an attention module, addressing the challenge of precise detection in scenarios where electrical equipment is densely arranged or tilted. We also introduce a thermal fault diagnosis approach that combines temperature differences with DeeplabV3 + semantic segmentation. The improved RetinaNet's recognition results are fed into the DeeplabV3 + model to further segment structures prone to thermal faults. The accuracy of component recognition in this paper achieved 87.23%, 86.54%, and 90.91%, with respective false alarm rates of 7.50%, 8.20%, and 7.89%. We propose a comprehensive method spanning from preprocessing through target recognition to thermal fault diagnosis for infrared images of complex electrical equipment, providing practical insights and robust solutions for future automation of electrical equipment inspections. [ABSTRACT FROM AUTHOR]

Deep convolutional neural networks (CNNs) have demonstrated significant potential in enhancing image denoising performance. However, most denoising methods fuse different levels of features through long and short skip connections, easily generating a lot of redundant information, thereby weakening the complementarity of different levels of features, resulting in the loss of image details. In this paper, we propose a multi-scale detail–noise complementary learning (MDNCL) network for additive white Gaussian noise removal and real-world noise removal. The MDNCL network comprises two branches, namely the Detail Feature Learning Branch (DLB) and the Noise Learning Branch (NLB). Specifically, a loss function is applied to guide the complementary learning of image detail features and noisy mappings in these two branches. This learning approach effectively balances noise reduction and detail restoration, especially when dealing with high ratios of noise. To enhance the complementarity of features between different network layers and avoid redundant information, we designed a Feature Subtraction Unit (FSU) to capture the differences in features across the DLB network layers. Our extensive experimental evaluations demonstrate that the MDNCL approach achieves impressive denoising performance and outperforms other popular denoising methods. [ABSTRACT FROM AUTHOR]

Image denoising and super-resolution play vital roles in imaging systems, greatly reducing the preprocessing cost of many AI techniques for object detection, segmentation, and tracking. Various advancements have been accomplished in this field, but progress is still needed. In this paper, we have proposed a novel technique named the Enhanced Learning Enriched Features (ELEF) mechanism using a deep convolutional neural network, which makes significant improvements to existing techniques. ELEF consists of two major processes: (1) Denoising, which removes the noise from images; and (2) Super-resolution, which improves the clarity and details of images. Features are learned through deep CNN and not through traditional algorithms so that we can better refine and enhance images. To effectively capture features, the network architecture adopted Dual Attention Units (DUs), which align with the Multi-Scale Residual Block (MSRB) for robust feature extraction, working sidewise with the feature-matching Selective Kernel Extraction (SKF). In addition, resolution mismatching cases are processed in detail to produce high-quality images. The effectiveness of the ELEF model is highlighted by the performance metrics, achieving a Peak Signal-to-Noise Ratio (PSNR) of 42.99 and a Structural Similarity Index (SSIM) of 0.9889, which indicates the ability to carry out the desired high-quality image restoration and enhancement. [ABSTRACT FROM AUTHOR]

During underwater image processing, image quality is affected by the absorption and scattering of light in water, thus causing problems such as blurring and noise. As a result, poor image quality is unavoidable. To achieve overall satisfying research results, underwater image denoising is vital. This paper presents an underwater image denoising method, named HHDNet, designed to address noise issues arising from environmental interference and technical limitations during underwater robot photography. The method leverages a dual-branch network architecture to handle both high and low frequencies, incorporating a hybrid attention module specifically designed for the removal of high-frequency abrupt noise in underwater images. Input images are decomposed into high-frequency and low-frequency components using a Gaussian kernel. For the high-frequency part, a Global Context Extractor (GCE) module with a hybrid attention mechanism focuses on removing high-frequency abrupt signals by capturing local details and global dependencies simultaneously. For the low-frequency part, efficient residual convolutional units are used in consideration of less noise information. Experimental results demonstrate that HHDNet effectively achieves underwater image denoising tasks, surpassing other existing methods not only in denoising effectiveness but also in maintaining computational efficiency, and thus HHDNet provides more flexibility in underwater image noise removal. [ABSTRACT FROM AUTHOR]

To address the problems of noise interference and image blurring in hyperspectral imaging (HSI), this paper proposes a denoising method for HSI based on deep learning and a total variation (TV) prior. The method minimizes the first-order moment distance between the deep prior of a Fast and Flexible Denoising Convolutional Neural Network (FFDNet) and the Enhanced 3D TV (E3DTV) prior, obtaining dual priors that complement and reinforce each other's advantages. Specifically, the original HSI is initially processed with a random binary sparse observation matrix to achieve a sparse representation. Subsequently, the plug-and-play (PnP) algorithm is employed within the framework of generalized alternating projection (GAP) to denoise the sparsely represented HSI. Experimental results demonstrate that, compared to existing methods, this method shows significant advantages in both quantitative and qualitative assessments, effectively enhancing the quality of HSIs. [ABSTRACT FROM AUTHOR]

Image denoising is a fundamental problem in computer vision (CV). Vision Transformer (ViT) is an improvement in CV after convolutional neural networks (CNNs). Recently, it has been demonstrated that the RepVGG network of the structural re-parameterization performs well in image tasks. Experiments indicate that the ViT-based uniformer transformer network has successfully balanced local and global information. As image denoising tasks require more local and global information about the image, a novel image denoising model named structural Re-parameterization Uniformer Transformer-UNet (Rep-UUNet) is proposed in this paper. The model is structural and re-parameterized the structure of the Uniformer Transformer network and uses the UNet skip connections to reconstruct the output image. To complete image downstream tasks, the RepVGG model which utilizes the image local information is used. Indicators such as PSNR, SSIM and others are used to assess the performance of image denoising. Experimental results demonstrate that our Rep-UUNet network model outperforms other five models. [ABSTRACT FROM AUTHOR]

Preprocessing plays a key role in Raman spectral analysis. However, classical preprocessing algorithms often have issues with reducing Raman peak intensities and changing the peak shape when processing spectra. This paper introduces a unified solution for preprocessing based on a convolutional autoencoder to enhance Raman spectroscopy data. One is a denoising algorithm that uses a convolutional denoising autoencoder (CDAE model), and the other is a baseline correction algorithm based on a convolutional autoencoder (CAE+ model). The CDAE model incorporates two additional convolutional layers in its bottleneck layer for enhanced noise reduction. The CAE+ model not only adds convolutional layers at the bottleneck but also includes a comparison function after the decoding for effective baseline correction. The proposed models were validated using both simulated spectra and experimental spectra measured with a Raman spectrometer system. Comparing their performance with that of traditional signal processing techniques, the results of the CDAE-CAE+ model show improvements in noise reduction and Raman peak preservation. [ABSTRACT FROM AUTHOR]

Remote sensing image denoising is of great significance for the subsequent use and research of images. Gaussian noise and salt-and-pepper noise are prevalent noises in images. Contemporary denoising algorithms often exhibit limitations when addressing such mixed noise scenarios, manifesting in suboptimal denoising outcomes and the potential blurring of image edges subsequent to the denoising process. To address the above problems, a second-order removal method for mixed noise in remote sensing images was proposed. In the first stage of the method, dilated convolution was introduced into the DnCNN (denoising convolutional neural network) network framework to increase the receptive field of the network, so that more feature information could be extracted from remote sensing images. Meanwhile, a DropoutLayer was introduced after the deep convolution layer to build the noise reduction model to prevent the network from overfitting and to simplify the training difficulty, and then the model was used to perform the preliminary noise reduction on the images. To further improve the image quality of the preliminary denoising results, effectively remove the salt-and-pepper noise in the mixed noise, and preserve more image edge details and texture features, the proposed method employed a second stage on the basis of adaptive median filtering. In this second stage, the median value in the original filter window median was replaced by the nearest neighbor pixel weighted median, so that the preliminary noise reduction result was subjected to secondary processing, and the final denoising result of the mixed noise of the remote sensing image was obtained. In order to verify the feasibility and effectiveness of the algorithm, the remote sensing image denoising experiments and denoised image edge detection experiments were carried out in this paper. When the experimental results are analyzed through subjective visual assessment, images denoised using the proposed method exhibit clearer and more natural details, and they effectively retain edge and texture features. In terms of objective evaluation, the performance of different denoising algorithms is compared using metrics such as mean square error (MSE), peak signal-to-noise ratio (PSNR), and mean structural similarity index (MSSIM). The experimental outcomes indicate that the proposed method for denoising mixed noise in remote sensing images outperforms traditional denoising techniques, achieving a clearer image restoration effect. [ABSTRACT FROM AUTHOR]

Wetland experimental images are often affected by factors such as waves, weather conditions, and lighting, resulting in severe noise in the images. In order to improve the quality and accuracy of wetland experimental images, this paper proposes a wetland experimental image denoising method based on the fast finite shearlet transform (FFST) and a deep convolutional neural network model. The FFST is used to decompose the wetland experimental images, which can capture the features of different frequencies and directions in the images. The network model has a deep network structure and powerful feature extraction capabilities. By training the model, it can learn the relevant features in the wetland experimental images, thereby achieving denoising effects. The experimental results show that, compared to traditional denoising methods, the proposed method in this paper can effectively remove noise from wetland experimental images while preserving the details and textures of the images. This is of great significance for improving the quality and accuracy of wetland experimental images. [ABSTRACT FROM AUTHOR]

Background: Image denoising technology removes noise from the corrupted image by utilizing different features between image and noise. Convolutional neural network (CNN)-based algorithms have been the concern of the recent progress on diverse image restoration problems and become an efficient solution in image denoising. Objective: Although a quite number of existing CNN-based image denoising methods perform well on the simplified additive white Gaussian noise (AWGN) model, their performance often degrades severely on the real-world noisy images which are corrupted by more complicated noise. Methods: In this paper, we utilized the multi-task learning (MTL) framework to integrate multiple loss functions for collaborative training of CNN. This approach aims to improve the denoising performance of CNNs on real-world images with non-Gaussian noise. Simultaneously, to automatically optimize the weights of individual sub-tasks within the MTL framework, we incorporated a self-learning weight layer into the CNN. Results: Extensive experiments demonstrate that our approach effectively enhances the denoising performance of CNN-based image denoising algorithms on real-world images. It reduces excessive image smoothing, improves quantitative metrics, and enhances visual quality in the restored images. Conclusion: Our method shows the effectiveness of the improved performance of denoising CNNS for real-world image denoising processing. [ABSTRACT FROM AUTHOR]

The image denoising model based on convolutional neural network (CNN) can achieve a good denoising effect. However, its robustness is poor, and it is not suitable for direct noise removal tasks. Differently, the image denoising method based on the diffusion equation is more stable and has theoretical guarantees. In order to give full play to the advantages of CNN and diffusion equation in image denoising, this paper proposes a speckle noise denoising model via a combination of the two tools. Firstly, based on the mathematical model of speckle noise, a class of neural network speckle noise removal model which mixes residual learning and structure learning is proposed using image decomposition theory. Then, in order to solve the hyperparameter problem that the model depends on noise variance, a noise variance estimation algorithm based on a nonlinear diffusion equation is proposed. Finally, a speckle noise denoising model based on diffusion equation and CNN is obtained. Numerical simulation experiments verify the accuracy of the variance estimation algorithm and also the denoising effect and practical application value of the proposed method. [ABSTRACT FROM AUTHOR]

Recently, convolutional neural networks (CNNs) have been widely used in image denoising, and their performance has been enhanced through residual learning. However, previous research mostly focused on optimizing the network architecture of CNNs, ignoring the limitations of the commonly used residual learning. This paper identifies two of its limitations, which are the neglect of image information and the lack of effective consideration of image self-similarity. To solve these limitations, this paper proposes a compositional denoising network (CDN), which contains two sub-paths, the image information path (IIP) and the noise estimation path (NEP), respectively. IIP is trained via an image-to-image method to extract image information. For NEP, it utilizes image self-similarity from the perspective of training. This similarity-based training method constrains NEP to output similar estimated noise distributions for different image patches with a specific kind of noise. Finally, image information and noise distribution information are comprehensively considered for image denoising. Experimental results indicate that CDN outperforms other CNN-based methods in both synthetic and real-world image denoising, achieving state-of-the-art performance. [ABSTRACT FROM AUTHOR]

Most studies on convolutional Neural Network (CNN) based image processing have proposed networks that can be optimized for a single level. Here, the term "level" refers to the specific objective defined for each task, such as the degree of noise in denoising tasks. Hence, they underperform on other levels and must be retrained to deliver optimal performance. Using multiple models to cover multiple levels involves very high computational costs. To solve these problems, recent approaches train the networks on two different levels and propose their own modulation methods to enable the arbitrary intermediate levels. However, many of them 1) have difficulty adapting from one level to the other, 2) suffer from unintended artifacts in the intermediate levels, or 3) require large memory and computational cost. In this paper, we propose a novel framework using Filter Transition Network (FTN), which is a non-linear module that easily adapts to new levels, is regularized to prevent undesirable side-effects, and extremely lightweight being a data-independent module. Additionally, for stable learning of FTN, we newly propose a method to initialize nonlinear CNNs with identity mappings. Extensive results for various image processing tasks indicate that the performance of FTN is stable regarding adaptation and modulation and is comparable to that of the other heavy frameworks. [ABSTRACT FROM AUTHOR]

Remote sensing (RS) scene classification has always attracted much attention as an elemental and hot topic in the RS community. In recent years, many methods using convolutional neural networks (CNNs) and other advanced machine-learning techniques have been proposed. Their performance is excellent; however, they are disabled when there are noisy labels (i.e., RS scenes with incorrect labels), which is inevitable and common in practice. To address this problem, some specific RS classification models have been developed. Although feasible, their behavior is still limited by the complex contents of RS scenes, excessive noise filtering schemes, and intricate noise-tolerant learning strategies. To further enhance the RS classification results under the noisy scenario and overcome the above limitations, in this paper we propose a multiscale information exploration network (MIEN) and a progressive learning algorithm (PLA). MIEN involves two identical sub-networks whose goals are completing the classification and recognizing possible noisy RS scenes. In addition, we develop a transformer-assistive multiscale fusion module (TAMSFM) to enhance MIEN's behavior in exploring the local, global, and multiscale contents within RS scenes. PLA encompasses a dual-view negative-learning (DNL) stage, an adaptively positive-learning (APL) stage, and an exhaustive soft-label-learning (ESL) stage. Their aim is to learn the relationships between RS scenes and irrelevant semantics, model the links between clean RS scenes and their labels, and generate reliable pseudo-labels. This way, MIEN can be thoroughly trained under the noisy scenario. We simulate noisy scenarios and conduct extensive experiments using three public RS scene data sets. The positive experimental results demonstrate that our MIEN and PLA can fully understand RS scenes and resist the negative influence of noisy samples. [ABSTRACT FROM AUTHOR]

The main objective of this paper is to improve the efficiency and accuracy of convolutional neural network models for image classification and denoising tasks. The focus of the study is on enhancing the activation layer of these models, which is a critical component that determines the output of each neuron in the network. To achieve this goal, we propose a novel activation function based on Chebyshev polynomials, which is both data-driven and self-learnable. In addition to proposing the LIP model, the authors investigate its performance in approximating various nonlinearities and determine its Lipschitz bound. The study then evaluates the performance of the proposed activation function by conducting experiments on multiple datasets using different convolutional neural network models. The results show that the proposed activation function outperforms other activation layers and significantly enhances the accuracy of image classification and denoising tasks. [ABSTRACT FROM AUTHOR]

Deep convolutional neural networks (DCNN) have attracted considerable interest in image denoising because of their excellent learning capacity. However, most of the existing methods cannot fully extract and utilize the fine features during denoising, resulting in insufficient detailed information extracted and limited model expression ability, especially in complex denoising tasks. Inspired by the above challenges, in this paper, a feature-enhanced multi-scale residual network (FEMRNet) is proposed, mainly including an enhanced feature extraction block (EFEB), a multi-scale residual backbone (MSRB), a detail information recovery block (DIRB) and a merge reconstruction block (MRB). Specifically, the EFEB can increase the receptive field through dilated convolution with different expansion factors, and multi-scale convolution can further enhance the feature. The MSRB integrates global and local feature information through residual denoising blocks and skip connections to enhance the inferencing ability of denoising models. The DIRB is used to finely extract the information in the image, and combine the timing information by convGRU to restore the image details. Finally, MRB is designed to construct a clean image by subtracting the fused noise mapping obtained from MSRB and DIRB with a given noisy image. Additionally, extensive experiments are implemented on commonly-used denoising benchmarks. Comparison experiments with state-of-the-art methods and ablation experiments show that our method achieves promising performance in denoising tasks. [ABSTRACT FROM AUTHOR]

Convolutional neural networks (CNNs) have demonstrated impressive results in additive white Gaussian noise removal due to their strong fitting ability. However, their performance in mixed noise removal remains unsatisfactory, primarily due to their limited receptive field that focuses only on the local features of images and disregards global information. To ameliorate this issue, recent state‐of‐the‐art approaches employ attention mechanism (AM) to capture the global information. However, most AM based methods still suffer from low computational efficiency. In this paper, a novel model named simple dual attention mechanism UNet (SDAUNet) for mixed noise removal is proposed. In SDAUNet, the UNet architecture is used to gradually acquire multi‐scale image features and provide a more comprehensive and accurate representation of the image features than other CNNs. A simple dual attention convolutional block is presented to acquire the global image features that can successfully capture image details with a low burden. The experimental results demonstrate that the SDAUNet model can achieve better measurement metrics and visual performance than other state‐of‐the‐art methods. [ABSTRACT FROM AUTHOR]

Denoising computed tomography (CT) medical images is crucial in preserving information and restoring images contaminated with noise. Standard filters have extensively been used for noise removal and fine details' preservation. During the transmission of medical images, noise degrades the visibility of anatomical structures and subtle abnormalities, making it difficult for radiologists to accurately diagnose and interpret medical conditions. In recent studies, an optimum denoising filter using the wavelet threshold and deep-CNN was used to eliminate Gaussian noise in CT images using the image quality index (IQI) and peak signal-to-noise ratio (PSNR). Although the results were better than those with traditional techniques, the performance resulted in a loss of clarity and fine details' preservation that rendered the CT images unsuitable. To address these challenges, this paper focuses on eliminating noise in CT scan images corrupted with additive Gaussian blur noise (AGBN) using an ensemble approach that integrates anisotropic Gaussian filter (AGF) and wavelet transform with a deep learning denoising convolutional neural network (DnCNN). First, the noisy image is denoised by AGF and Haar wavelet transform as preprocessing operations to eliminate AGBN. The DnCNN is then combined with AGF and wavelet for post-processing operation to eliminate the rest of the noises. Specifically, we used AGF due to its adaptability to edge orientation and directional information, which prevents blurring along edges for non-uniform noise distribution. Denoised images are evaluated using PSNR, mean squared error (MSE), and the structural similarity index measure (SSIM). Results revealed that the average PSNR value of the proposed ensemble approach is 28.28, and the average computational time is 0.01666 s. The implication is that the MSE between the original and reconstructed images is very low, implying that the image is restored correctly. Since the SSIM values are between 0 and 1.0, 1.0 perfectly matches the reconstructed image with the original image. In addition, the SSIM values at 1.0 or near 1.0 implicitly reveal a remarkable structural similarity between the denoised CT image and the original image. Compared to other techniques, the proposed ensemble approach has demonstrated exceptional performance in maintaining the quality of the image and fine details' preservation. [ABSTRACT FROM AUTHOR]

The prevalent convolutional neural network (CNN)-based image denoising methods extract features of images to restore the clean ground truth, achieving high denoising accuracy. However, these methods may ignore the underlying distribution of clean images, inducing distortions or artifacts in denoising results. This paper proposes a new perspective to treat image denoising as a distribution learning and disentangling task. Since the noisy image distribution can be viewed as a joint distribution of clean images and noise, the denoised images can be obtained via manipulating the latent representations to the clean counterpart. This paper also provides a distribution-learning-based denoising framework. Following this framework, we present an invertible denoising network, FDN, without any assumptions on either clean or noise distributions, as well as a distribution disentanglement method. FDN learns the distribution of noisy images, which is different from the previous CNN-based discriminative mapping. Experimental results demonstrate FDN's capacity to remove synthetic additive white Gaussian noise (AWGN) on both category-specific and remote sensing images. Furthermore, the performance of FDN surpasses that of previously published methods in real image denoising with fewer parameters and faster speed. [ABSTRACT FROM AUTHOR]

Considering the potential risk of X-ray to patients, denoising of low-dose X-ray medical images is imperative. Inspired by deep learning, a convolutional autoencoder method for X-ray breast image denoising is proposed in this paper. First, image symmetry and flip are used to increase the number of images in the public dataset; second, the number of samples is increased further by image cropping segmentation, adding simulated noise, and producing the dataset. Finally, a convolutional autoencoder neural network model is constructed, and clean and noisy images are fed into it to complete the training. The results show that this method effectively removes noise while retaining image details in X-ray breast images, yielding higher peak signal-to-noise ratio and structural similarity index values than classical and novel denoising methods. [ABSTRACT FROM AUTHOR]

Banana is a significant crop, and three banana leaf diseases, including Sigatoka, Cordana and Pestalotiopsis, have the potential to have a serious impact on banana production. Existing studies are insufficient to provide a reliable method for accurately identifying banana leaf diseases. Therefore, this paper proposes a novel method to identify banana leaf diseases. First, a new algorithm called K-scale VisuShrink algorithm (KVA) is proposed to denoise banana leaf images. The proposed algorithm introduces a new decomposition scale K based on the semi-soft and middle course thresholds, the ideal threshold solution is obtained and substituted with the newly established threshold function to obtain a less noisy banana leaf image. Then, this paper proposes a novel network for image identification called Ghost ResNeSt-Attention RReLU-Swish Net (GR-ARNet) based on Resnet50. In this, the Ghost Module is implemented to improve the network's effectiveness in extracting deep feature information on banana leaf diseases and the identification speed; the ResNeSt Module adjusts the weight of each channel, increasing the ability of banana disease feature extraction and effectively reducing the error rate of similar disease identification; the model's computational speed is increased using the hybrid activation function of RReLU and Swish. Our model achieves an average accuracy of 96.98% and a precision of 89.31% applied to 13,021 images, demonstrating that the proposed method can effectively identify banana leaf diseases.

In digital image processing, filtering noise is an important step for reconstructing a high-quality image for further processing such as object segmentation, object detection, and object recognition. Various image-denoising approaches, including median, Gaussian, and bilateral filters, are available in the literature. Since convolutional neural networks (CNN) are able to directly learn complex patterns and features from data, they have become a popular choice for image-denoising tasks. As a result of their ability to learn and adapt to various denoising scenarios, CNNs are powerful tools for image denoising. Some deep learning techniques such as CNN incorporate denoising strategies directly into the CNN model layers. A primary limitation of these methods is their necessity to resize images to a consistent size. This resizing can result in a loss of vital image details, which might compromise CNN's effectiveness. Because of this issue, we utilize a traditional denoising method as a preliminary step for noise reduction before applying CNN. To our knowledge, a comparative performance study of CNN using traditional and embedded denoising against a baseline approach (without denoising) is yet to be performed. To analyze the impact of denoising on the CNN performance, in this paper, firstly, we filter the noise from the images using traditional means of denoising method before their use in the CNN model. Secondly, we embed a denoising layer in the CNN model. To validate the performance of image denoising, we performed extensive experiments for both traffic sign and object recognition datasets. To decide whether denoising will be adopted and to decide on the type of filter to be used, we also present an approach exploiting the peak-signal-to-noise-ratio (PSNRs) distribution of images. Both CNN accuracy and PSNRs distribution are used to evaluate the effectiveness of the denoising approaches. As expected, the results vary with the type of filter, impact, and dataset used in both traditional and embedded denoising approaches. However, traditional denoising shows better accuracy, while embedded denoising shows lower computational time for most of the cases. Overall, this comparative study gives insights into whether denoising will be adopted in various CNN-based image analyses, including autonomous driving, animal detection, and facial recognition. [ABSTRACT FROM AUTHOR]

Deep convolution neural networks have proven their powerful ability in comparing many tasks of computer vision due to their strong data learning capacity. In this paper, we propose a novel end-to-end denoising network, termed Fourier embedded U-shaped network (FEUSNet). By analyzing the amplitude spectrum and phase spectrum of Fourier coefficients, we find that low-frequency features of an image are in the former while noise features are in the latter. To make full use of this characteristic, Fourier features are learned and are concatenated as a prior module that is embedded into a U-shaped network to reduce noise while preserving multi-scale fine details. In the experiments, we first present ablation studies on the Fourier coefficients' learning networks and loss function. Then, we compare the proposed FEUSNet with the state-of-the-art denoising methods in quantization and qualification. The experimental results show that our FEUSNet performs well in noise suppression and preserves multi-scale enjoyable structures, even outperforming advanced denoising approaches. [ABSTRACT FROM AUTHOR]

In this paper, we present a new method for multitask learning applied to ultrasound beamforming. Beamforming is a critical component in the ultrasound image formation pipeline. Ultrasound images are constructed using sensor readings from multiple transducer elements, with each element typically capturing multiple acquisitions per frame. Hence, the beamformer is crucial for framerate performance and overall image quality. Furthermore, post-processing, such as image denoising, is usually applied to the beamformed image to achieve high clarity for diagnosis. This work shows a fully convolutional neural network that can learn different tasks by applying a new weight normalization scheme. We adapt our model to both high frame rate requirements by fitting weight normalization parameters for the sub-sampling task and image denoising by optimizing the normalization parameters for the speckle reduction task. Our model outperforms single-angle delay and sum on pixel-level measures for speckle noise reduction, subsampling, and single-angle reconstruction. [ABSTRACT FROM AUTHOR]

In the field of image denoising, convolutional neural networks (CNNs) have become increasingly popular due to their ability to learn effective feature representations from large amounts of data. In the field of image denoising, CNNs are widely used to improve performance. However, increasing network depth can weaken the influence of shallow layers on deep layers, especially for complex denoising tasks such as real denoising and blind denoising, where conventional networks fail to achieve high-quality results. To address this issue, this paper proposes a hybrid dilated convolution-based denoising network (AMDNet) that incorporates attention mechanisms. In specific, AMDNet consists of four modules: the sparse module (SM), the feature fusion module (FFM), the attention guidance module (AGM), and the image residual module (IRM). The SM employs hybrid dilated convolution to extract local features, while the FFM is used to integrate global and local features. The AGM accurately extracts noise information hidden in complex backgrounds. Finally, the IRM reconstructs images in a residual manner to obtain high-quality results after denoising. AMDNet has the following features: (1) The sparse mechanism in hybrid dilated convolution enables better extraction of local features, enhancing the network's ability to capture noise information. (2) The feature fusion module, through long-range connections, fully integrates global and local features, improving the performance of the model; (3) the attention module is ingeniously designed to precisely extract features in complex backgrounds. The experimental results demonstrate that AMDNet achieves outstanding performance on three tasks (Gaussian noise, real noise, and blind denoising). [ABSTRACT FROM AUTHOR]

Image denoising is an important consideration for improving the accuracy of acquired data. Given the morphological nature of astrophotography, with sparse star data overlaid on dim underlying background nebulae and galaxies, denoising is complicated to implement. New methods using convolutional neural networks and transformer networks show promise in general denoising applications. This paper explores the use of five recent denoising networks on astrophotography image data and demonstrates the feasibility as well as optimisations and pitfalls in the use of these networks. The paper confirms that image denoising in astrophotography using such networks is accurate and that a complete knowledge of ground truth might not be necessary for the accurate training of them. [ABSTRACT FROM AUTHOR]

Abnormal target detection in hyperspectral remote sensing image is one of the hotspots in image research. The image noise generated in the detection process will lead to the decline of the quality of hyperspectral remote sensing image. In view of this, this paper proposes an abnormal target detection method of hyperspectral remote sensing image based on the convolution neural network. Firstly, the deep residual learning network model has been used to remove the noise in hyperspectral remote sensing image. Secondly, the spatial and spectral features of hyperspectral remote sensing images were used to optimize the clustering dictionary, and then the image segmentation containing target information is completed. Finally, the image was input into the deep convolution neural network with a dual classifier, and the network detects the abnormal target in the image. The test results of this algorithm show that the structural similarity of the denoised image is higher than 0.86, which shows that this method has good noise reduction performance, image details will not damage, segmentation effect is good, and it can obtain high-definition target image information and accurately detect abnormal targets in the image. [ABSTRACT FROM AUTHOR]

Recent years, thanks to the prosperous development of deep convolutional neural network, image denoising task has achieved unprecedented achievements. However, previous researches have difficulties in keeping the balance between noise removing and textual details preserving, even bringing the negative effect, such as local blurring. To overcome these weaknesses, in this paper, we propose an innovative multi-attention complementary fusion network (MACFNet) to restore delicate texture details while eliminating noise to the greatest extent. To be specific, our proposed MACFNet mainly composes of several multi-attention complementary fusion modules (MACFMs). Firstly, we use feature extraction block (FEB) to extract basic features.Then, we use spatial attention (SA), channel attention (CA) and patch attention (PA) three different kinds of attention mechanisms to extract spatial-dimensional, channel-dimensional and patch-dimensional attention aware features, respectively. In addition, we attempt to integrate three attention mechanisms in an effective way. Instead of directly concatenate, we design a subtle complementary fusion block (CFB), which is skilled in incorporating three sub-branches characteristics adaptively. Extensive experiments are carried out on gray-scale image denoising, color image denoising and real noisy image denoising. The quantitative results (PSNR) and visual effects all prove that our proposed network achieves great performance over some state-of-the-art methods. [ABSTRACT FROM AUTHOR]

Deep learning models with convolutional operators have received widespread attention for their good image denoising performance. However, since the convolutional operation prefers to extract local features, the extracted features may lose some global information, such as texture, structure, and color characteristics, when the object in the image is large. To address this issue, this paper proposes an N-shaped convolutional neural network with the ability to extract multi-scale features to capture more useful information and alleviate the problem of global information loss. The proposed network has two main parts: a multi-scale input layer and a multi-scale feature extraction layer. The former uses a two-dimensional Haar wavelet to create an image pyramid, which contains the corrupted image's high- and low-frequency components at different scales. The latter uses a U-shaped convolutional network to extract features at different scales from this image pyramid. The method sets the mean-squared error as the loss function and uses the residual learning strategy to learn the image noise directly. Compared with some existing image denoising methods, the proposed method shows good performance in gray and color image denoising, especially in textures and contours. [ABSTRACT FROM AUTHOR]

The demands of high-quality videos captured by camera become bigger due to the rapid development of pattern recognition and artificial intelligence. Video denoising is the key technology to obtain clear videos. However, the research on video denoising is far from enough now. In this paper, we propose a video denoising method based on convolutional neural network architecture to reduce the noise from the sensor system. We improve the loss function of noise estimation by imposing adaptive penalty on under-estimation error of noise level which makes our method perform robustly. Furthermore, we make use of multi-level features to guide the spatial denoising, where multilayer semantic information of the image is regarded as the perceptual loss. Instead of relying on Optical Flow solving the characterization of inter-frame information, we utilize U-Net-like structure to handle motion implicitly. It is less computationally expensive and avoids distortions caused by inaccurate flow and object occlusion. In order to locate temporal features and suppress useless information, the attention mechanism is introduced to the skip connections of the U-Net-like structure. Experimental results demonstrate that the proposed algorithm outputs more convincing results in both peak signal-to-noise ratio (PSNR) and structural similarity index measure (SSIM) indexes when processing Gaussian noise, synthetic real noise, and real noise compared with selected approaches. [ABSTRACT FROM AUTHOR]

Deep convolutional neural networks (CNN) with hierarchical architectures have obtained good results for image denoising. However, in some cases where the noise level is unknown and the image background is complex, it is challenging to obtain robust information through CNN. In this paper, we present a multi-level information fusion CNN (MLIFCNN) in image denoising containing a fine information extraction block (FIEB), a multi-level information interaction block (MIIB), a coarse information refinement block (CIRB), and a reconstruction block (RB). In order to adapt to more complex image backgrounds, FIEB uses parallel group convolution to extract wide-channel information. To enhance the robustness of the obtained information, a MIIB uses residual operations to act in two sub-networks for implementing the interaction of wide and deep information to adapt to the distribution of different noise levels. To enhance the stability of the training denoiser, CIRB stacks common and group convolutions to refine the obtained information. Finally, RB uses a residual operation to act in a single convolution in order to obtain the resultant clean image. Experimental results show that our method is better than many other excellent methods, both in terms of quantitative and qualitative aspects. [ABSTRACT FROM AUTHOR]

This paper proposes a deep learning-based patch label denoising method (LossDiff) for improving the classification of whole-slide images of cancer using a convolutional neural network (CNN). Automated whole-slide image classification is often challenging, requiring a large amount of labeled data. Pathologists annotate the region of interest by marking malignant areas, which pose a high risk of introducing patch-based label noise by involving benign regions that are typically small in size within the malignant annotations, resulting in low classification accuracy with many Type-II errors. To overcome this critical problem, this paper presents a simple yet effective method for noisy patch classification. The proposed method, validated using stomach cancer images, provides a significant improvement compared to other existing methods in patch-based cancer classification, with accuracies of 98.81%, 97.30% and 89.47% for binary, ternary, and quaternary classes, respectively. Moreover, we conduct several experiments at different noise levels using a publicly available dataset to further demonstrate the robustness of the proposed method. Given the high cost of producing explicit annotations for whole-slide images and the unavoidable error-prone nature of the human annotation of medical images, the proposed method has practical implications for whole-slide image annotation and automated cancer diagnosis. [ABSTRACT FROM AUTHOR]

Image denoising faces significant challenges, arising from the sources of noise. Specifically, Gaussian, impulse, salt, pepper, and speckle noise are complicated sources of noise in imaging. Convolutional neural network (CNN) has increasingly received attention in image denoising task. Several CNN methods for denoising images have been studied. These methods used different datasets for evaluation. In this paper, we offer an elaborate study on different CNN techniques used in image denoising. Different CNN methods for image denoising were categorized and analyzed. Popular datasets used for evaluating CNN image denoising methods were investigated. Several CNN image denoising papers were selected for review and analysis. Motivations and principles of CNN methods were outlined. Some state-of-the-arts CNN image denoising methods were depicted in graphical forms, while other methods were elaborately explained. We proposed a review of image denoising with CNN. Previous and recent papers on image denoising with CNN were selected. Potential challenges and directions for future research were equally fully explicated. [ABSTRACT FROM AUTHOR]

In this paper, we propose a novel deep convolutional neural network to solve the general multi-modal image restoration (MIR) and multi-modal image fusion (MIF) problems. Different from other methods based on deep learning, our network architecture is designed by drawing inspirations from a new proposed multi-modal convolutional sparse coding (MCSC) model. The key feature of the proposed network is that it can automatically split the common information shared among different modalities, from the unique information that belongs to each single modality, and is therefore denoted with CU-Net, i.e., common and unique information splitting network. Specifically, the CU-Net is composed of three modules, i.e., the unique feature extraction module (UFEM), common feature preservation module (CFPM), and image reconstruction module (IRM). The architecture of each module is derived from the corresponding part in the MCSC model, which consists of several learned convolutional sparse coding (LCSC) blocks. Extensive numerical results verify the effectiveness of our method on a variety of MIR and MIF tasks, including RGB guided depth image super-resolution, flash guided non-flash image denoising, multi-focus and multi-exposure image fusion. [ABSTRACT FROM AUTHOR]

In this paper, we study how to denoise medical ultrasound images and improve the performance of instance segmentation using deep learning technology. Since medical ultrasound images usually contain a lot of noises, we first propose a novel unsupervised learning approach called Dual Image (DI) for denoising of medical ultrasound images. DI consists of three main features. Firstly, unlike many existing supervised denoising methods, it does not need clean medical ultrasound images for denoising. Instead, it uses Computed Tomography (CT) images and the noise patches extracted from medical ultrasound images for denoising. Secondly, to effectively select noise patches from medical ultrasound images, a patch selection algorithm based on entropy is formulated. Thirdly, to minimize structure variation of denoised medical ultrasound images, a new reconstruction block is designed for combining the structural information from the structural enhancement block. After denoising, since medical ultrasound images are usually poor in features, to further improve the instance segmentation performance, we extend SOLOv2 to Segmenting on Ultrasound Image (SOUI) by proposing the Double Feature Pyramid Network (D-FPN) and mask fusion branch to strengthen the communication and fusion of different feature layers. Extensive experiments have been performed to study the performance of DI and SOUI using practical medical ultrasound images. We demonstrate that DI can greatly improve the quality of medical ultrasound images and minimize structure variation of denoised medical ultrasound images. SOUI gives 53.7% AP(Average Precision) on practical medical ultrasound images, and outperforms most the state-of-the-art instance segmentation methods including SOLOv2. Code is available at: https://github.com/ztt0821/SOUI. [ABSTRACT FROM AUTHOR]

Deep convolutional neural networks (DCNN) have been widely used in the field of image denoising because of their fast inference and good performance. However, the design of networks for the DCNN is mostly empirical, and the interpretation and robustness of them remains a major challenge. Inspired by the total generalized variation method, this paper proposes a novel adaptive denoising network. It mainly improves the TGV algorithm in terms of two points. Firstly, the first and second order derivation term are replaced by the learnable operators. Secondly, the regularization terms are learned from the training data by using convolution networks other than the fixed ones. The network design derived from the process for tackling the denoising problem based on the primal-dual hybrid gradient optimization algorithm, is called TGVLNet- Total Generalized Variational-Liked Network, which allows for the image prior and the linear operators to be tuned differently in each iteration, and enhances the flexibility and generalization ability of the network. The experiment results of Gaussian noise removal and signal dependent noise removal manifest that the proposed network has superior performance and generalization. Compared to most of the blind denoising methods with additive white Gaussian noise, the proposed TGVLNet performs better in unseen noise level. It is noting that we train the model only on the synthetic image of the signal dependent noise removal, and use the model to remove the noise of some images on two real datasets, i.e NC12 and Nam, the denoising results also presents much better visual quality and performance, which further verify the generalization and robustness of our method. [ABSTRACT FROM AUTHOR]

The use of convolutional neuronal networks (CNN) for the treatment of interferometric fringes has been introduced in recent years. In this paper, we optimize and build a CNN model, based U-NET architecture, to maximize its performance processing electronic speckle interferometry fringes (ESPI). The proposed approach is based on quick and light trainings to select the architecture parameters (network depth and kernel sizes) to maximize the performance of the neural network improving the visibility of ESPI images. To measure the performance, the structural similarity index (SSMI) will be the lead indicator, and the need for large datasets to train neural networks, unavailable for ESPI images, forces the use of a simulated ESPI image dataset along the process. This dataset is computed using Zernike polynomials to simulate local surface deformations in the specimen under test and simulated true speckle fields for the reference and object field involved in ESPI techniques. [ABSTRACT FROM AUTHOR]

The task of partitioning convex shape objects from images is a hot research topic, since this kind of object can be widely found in natural images. The difficulties in achieving this task lie in the fact that these objects are usually partly interrupted by undesired background scenes. To estimate the whole boundaries of these objects, different neural networks are designed to ensure the convexity of corresponding image segmentation results. To make use of well-trained neural networks to promote the performances of convex shape image segmentation tasks, in this paper a new image segmentation model is proposed in the variational framework. In this model, a fuzzy membership function, instead of a classical binary label function, is employed to indicate image regions. To ensure fuzzy membership functions can approximate to binary label functions well, an edge-preserving smoothness regularizer is constructed from an off-the-shelf plug-and-play network denoiser, since an image denoising process can also be seen as an edge-preserving smoothing process. From the numerical results, our proposed method could generate better segmentation results on real images, and our image segmentation results were less affected by the initialization of our method than the results obtained from classical methods. [ABSTRACT FROM AUTHOR]

The automatic recognition of CT (Computed Tomography) images of liver cancer is important for the diagnosis and treatment of early liver cancer. However, there are problems such as single model structure and loss of pooling layer information when using a traditional convolutional neural network to recognize CT images of liver cancer. Therefore, this paper proposes an efficient method for liver CT image recognition based on the capsule network (CapsNet). Firstly, the liver CT images are preprocessed, and in the process of image denoising, the traditional non-local mean (NLM) denoising algorithm is optimized with a superpixel segmentation algorithm to better protect the information of image edges. After that, CapsNet was used for image recognition for liver CT images. The experimental results show that the average recognition rate of liver CT images reaches 92.9% when CapsNet is used, which is 5.3% higher than the traditional CNN model, indicating that CapsNet has better recognition accuracy for liver CT images. [ABSTRACT FROM AUTHOR]

In today's busy world, people are shifting to online shopping for their basic needs like groceries, clothes, etc. As a result, modern applications are incorporating image search as part of their application. However, retrieving accurate fashion images is challenging due to the low inter-class separability. In this paper, we aim to propose and implement three deep learning denoising autoencoder image retrieval models trained on Fashion-MNIST dataset and MNIST dataset. Fashion-MNIST is widely used datasets to evaluate model because of its low inter-class separability. It is difficult to get a good accuracy on Fashion-MNIST dataset. The proposed models train to learn significant features of fashion images and improve the precision of image retrieval. These models are compared based on the Label Ranking Average Precision (LRAP) values, with the third model bagging the highest LRAP value. These models also trained and tested on MNIST dataset, which have similar configuration as Fashion-MNIST dataset but with high inter-class separability. Intriguingly, the second and third models trained on MNIST dataset demonstrate neck-to-neck performance, both achieving comparable accuracy. This choice of dataset ensures that the models' accuracy is more noticeable and significant. The outcomes of this study validate the efficacy of denoising autoencoders in fashion image retrieval and emphasize their prospective in improving online shopping experiences. [ABSTRACT FROM AUTHOR]

The rapid development of neural network technology promotes the neural network model driven by big data to overcome the texture effect of complex objects. Due to the limitations in complex scenes, it is necessary to establish custom template matching and apply it to the research of many fields of computational vision technology. The dependence on high-quality small label sample database data is not very strong, and the machine learning system of deep feature connection to complete the task of texture effect inference and speculation is relatively poor. The style transfer algorithm based on neural network collects and preserves the data of patterns, extracts and modernizes their features. Through the algorithm model, it is easier to present the texture color of patterns and display them digitally. In this paper, according to the texture effect reasoning of custom template matching, the 3D visualization of the target is transformed into a 3D model. The high similarity between the scene to be inferred and the user-defined template is calculated by the user-defined template of the multi-dimensional external feature label. The convolutional neural network is adopted to optimize the external area of the object to improve the sampling quality and computational performance of the sample pyramid structure. The results indicate that the proposed algorithm can accurately capture the significant target, achieve more ablation noise, and improve the visualization results. The proposed deep convolutional neural network optimization algorithm has good rapidity, data accuracy and robustness. The proposed algorithm can adapt to the calculation of more task scenes, display the redundant vision-related information of image conversion, enhance the powerful computing power, and further improve the computational efficiency and accuracy of convolutional networks, which has a high research significance for the study of image information conversion. [ABSTRACT FROM AUTHOR]

Images under low-light conditions suffer from noise, blurring, and low contrast, thus limiting the precise detection of objects. For this purpose, a novel method is introduced based on convolutional neural network (CNN) dual attention unit (DAU) and selective kernel feature synthesis (SKFS) that merges with the Retinex theory-based model for the enhancement of dark images under low-light conditions. The model mentioned in this paper is a multi-scale residual block made up of several essential components equivalent to an onward convolutional neural network with a VGG16 architecture and various Gaussian convolution kernels. In addition, backpropagation optimizes most of the parameters in this model, whereas the values in conventional models depend on an artificial environment. The model was constructed using simultaneous multi-resolution convolution and dual attention processes. We performed our experiment in the Tesla T4 GPU of Google Colab using the Customized Raw Image Dataset, College Image Dataset (CID), Extreme low-light denoising dataset (ELD), and ExDark dataset. In this approach, an extended set of features is set up to learn from several scales to incorporate contextual data. An extensive performance evaluation on the four above-mentioned standard image datasets showed that MSR-MIRNeT produced standard image enhancement and denoising results with a precision of 97.33%; additionally, the PSNR/SSIM result is 29.73/0.963 which is better than previously established models (MSR, MIRNet, etc.). Furthermore, the output of the proposed model (MSR-MIRNet) shows that this model can be implemented in medical image processing, such as detecting fine scars on pelvic bone segmentation imaging, enhancing contrast for tuberculosis analysis, and being beneficial for robotic visualization in dark environments. [ABSTRACT FROM AUTHOR]

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Enhancing the visibility in extreme low-light environments is a challenging task. Under nearly lightless condition, existing image denoising methods could easily break down due to significantly low SNR. In this paper, we systematically study the noise statistics in the imaging pipeline of CMOS photosensors, and formulate a comprehensive noise model that can accurately characterize the real noise structures. Our novel model considers the noise sources caused by digital camera electronics which are largely overlooked by existing methods yet have significant influence on raw measurement in the dark. It provides a way to decouple the intricate noise structure into different statistical distributions with physical interpretations. Moreover, our noise model can be used to synthesize realistic training data for learning-based low-light denoising algorithms. In this regard, although promising results have been shown recently with deep convolutional neural networks, the success heavily depends on abundant noisy-clean image pairs for training, which are tremendously difficult to obtain in practice. Generalizing their trained models to images from new devices is also problematic. Extensive experiments on multiple low-light denoising datasets – including a newly collected one in this work covering various devices – show that a deep neural network trained with our proposed noise formation model can reach surprisingly-high accuracy. The results are on par with or sometimes even outperform training with paired real data, opening a new door to real-world extreme low-light photography. [ABSTRACT FROM AUTHOR]

For many decades researchers have been trying to make computer analysis of images as effective as the human vision system is. In [[1]] CIELab, a color-based component substitution pan sharpening algorithm is proposed for pan sharpening of the Pleiades Very High Resolution images. They evaluated twelve pan-sharpening algorithms, and experimentally proved that the Local Mean and Variance Matching (IMVM) algorithm was the best in terms of spectral consistency and synthesis. References 1 Rahimzadeganasl A., Alganci U., Goksel C. An Approach for the Pan Sharpening of Very High Resolution Satellite Images Using a CIELab Color Based Component Substitution Algorithm. [Extracted from the article]

As a powerful statistical signal modeling technique, sparse representation has been widely used in various image restoration (IR) applications. The sparsity-based methods have achieved leading performance in the past few decades. However, in recent years it has been surpassed by other methods, especially the recent deep learning based methods. In this paper, we address the question that whether sparse representation can be competitive again. The way we answer this question is to redesign it with a deep architecture. To be specific, we propose an end-to-end deep architecture that follows the process of the sparse representation based IR. In particular, we learn a sparse convolutional dictionary to replace the traditional dictionary, and a convolutional neural network (CNN) denoising prior to replace the image prior. Through end-to-end training, the parameters in convolutional dictionary and CNN denoiser can be jointly optimized. Experimental results on several representative IR tasks, including image denoising, deblurring and super-resolution, demonstrate that the proposed deep network can achieve superior performance against state-of-the-art model-based and learning-based methods. [ABSTRACT FROM AUTHOR]

Due to occlusion among views and temporal inconsistency in depth video, spatio-temporal distortion occurs in 3D synthesized video with depth image-based rendering. In this paper, we propose a deep Convolutional Neural Network (CNN)-based synthesized video denoising algorithm to reduce temporal flicker distortion and improve perceptual quality of 3D synthesized video. First, we analyze the spatio-temporal distortion, and model eliminating spatio-temporal distortion as a perceptual video denoising problem. Then, a deep learning-based synthesized video denoising network is proposed, in which a CNN-friendly spatio-temporal loss function is derived from a synthesized video quality metric and integrated with a single image denoising network architecture. Finally, specific schemes, i.e., specific Synthesized Video Denoising Networks (SynVD-Nets), and a general scheme, i.e., General SynVD-Net (GSynVD-Net), based on existing CNN-based denoising models, are developed to handle synthesized video with different distortion levels more effectively. Experimental results show that the proposed SynVD-Net and GSynVD-Net can outperform deep learning-based counterparts and conventional denoising methods, and significantly enhance perceptual quality of 3D synthesized video. [ABSTRACT FROM AUTHOR]

Generally, there are mainly two methods to solve the image restoration task in low-level computer vision, i.e., the model-based optimization method and the discriminative learning method. However, these two methods have clear advantages and disadvantages. For example, it is flexible for the model-based optimization method to handle different problems, but large quantity of computing time is required for better performance. The discriminative learning approach has high computing efficiency, but the application scope is seriously limited by the fixed training model. It would be better to combine the advantages of these two methods. Luckily, with the variable splitting techniques, we insert the trained convolutional neural network (CNN) for denoising as one model to the model-based optimization method to solve other image restoration problems (e.g., deblurring and super-resolution). Final experimental results show that our denoising network is able to provide strong prior information for image restoration tasks. The image restoration effects can reach or approximate the most advanced algorithm in such three tasks as denoising, deblurring, and super-resolution. Moreover, the algorithm proposed in this paper is also the most competitive in terms of computational efficiency. [ABSTRACT FROM AUTHOR]