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The time-of-flight (TOF) information makes Orlov’s condition not necessary for positron emission tomography (PET) imaging. One-view imaging has never been attempted before. This article investigates whether one-view imaging is possible. We claim that it is possible to obtain an image with measurements from only one-view, provided the TOF time resolution is good enough and the photon counts are high enough. In fact, the image value at a point can be reconstructed by measurements along one line of response (LOR) that passes through the point of interest. The region of interest (ROI) can be reconstructed with ray-by-ray deblurring methods, which make TOF PET a local tomography system. One-view imaging is a severely ill-posed problem if the TOF resolution is not good enough. One indicator of an ill-posed problem is the ringing artifacts in the reconstruction. We show that ringing artifacts can be eliminated by using the nonnegativity constraint for the dot-like objects with a zero background.

Suppression of ringing artifacts in images is a well-known image restoration problem. Gibbs-ringing artifacts occur when, in the process of magnetic resonance imaging, the source data from the frequency domain are mapped onto the spatial domain by using the discrete Fourier transform. The artifacts are caused by the incompleteness of these data, which, in turn, is due to cutting off the high frequencies of the Fourier spectrum. In this paper, we propose a hybrid method for Gibbs-ringing artifact suppression in magnetic resonance images that combines a deep learning model and a classical non-machine-learning algorithm for Gibbs-ringing artifact suppression based on optimal subvoxel shifts.

Medical image compression plays a vital role in diagnosis of diseases which allowing manipulation, efficient, transmission and storage of color, binary and grayscale image. Before transmission and storage, a medical image may be required to be compressed. The objective of the study is to develop an efficient and effective technique for digital medical images which alleviates the blocking artifacts from grayscale image while retaining all relevant structures. In this paper, we demonstrate a highly engineered postprocessing filtering approach has been designed to remove blocking effects from medical images at low bit rate. The proposed technique is comprised of three strategies i.e. 1) a threshold valve scheme which is used to capture the pixel vectors containing blocking artifacts. 2) Blocking artifacts measurement techniques. The blocking artifacts are measured by three frequency related modes (low, Moderate and high frequency model). 3) A directional filter which is used to remove over-smoothing and ringing artifacts near edges of block boundary. The algorithm is tested on digital medical grayscale images from different modalities. The experimental results illustrate that the proposed technique is more efficient on the basis of PSNR-B, MSSIM, and MOS indices than the state-of-the-art methods. The proposed algorithm can be seamlessly applied in area of medical image compression which high transmission efficiency and acceptable image quality can be guaranteed.

Multi-spectralimages suffers from out-of-focusblur due to well focused camera at the reference imaging channel. A framework for out-of-focus images de-blurring using texture extraction and modified Fourier transform is proposed. The texture is extracted from the blurred image using region covariance. Fourier transform is modified by modification of guided image (as prior) using L0 gradient projection followed by detail amplification which is used as an input for Fourier transform. For further enhancement, the multi-spectral de-blurred images are smooth, and ringing artifacts are minimized by combining with the textures extracted to ensure maximum edge preservation. Comparison of proposed and existing schemes on different multi-spectral images explains the advantage of the proposed scheme for de-blurring in terms of edge preservation, noise and artifacts removal.

In this paper, we propose a high quality image deblurring method that uses discrete cosine transform (DCT) and requires less computational complexity. We train our model on a new dataset which is customized to include images with large motion blurs. Recently, Convolutional Neural Network (CNN) and Generative Adversarial Network (GAN) based algorithms have been proposed for image deblurring. Moreover, multi-scale and multi-patch architectures of CNN restore blurred images clearly and suppress more ringing or blocking artifacts, but they take a longer time to process. To improve the quality of deblured images and reduce the computational time, we propose a method called “DeblurDCTGAN” that preserves texture and suppresses ringing artifacts in the restored image without multi-scale or multi-patch architecture using DCT based loss. This loss compares the restored image and the ground truth image in the frequency domain. With this loss, DeblurDCTGAN can reduce block noise and ringing artifacts while maintaining deblurring performance. Our experimental results show that DeblurDCTGAN gets the highest performances in terms of PSNR, SSIM, and running time compared with conventional methods. In terms of real image datasets, DeblurDCTGAN shows a better performance by using a customized training dataset made from GoPro, DVD, NFS and HIDE training datasets. Experimented code with pre-trained weights, datasets and results are available at https://github.com/Hiroki-Tomosada/DCTGAN-master

Face images deblurring has achieved advanced development; however, existing methods involve high computational cost problems. Furthermore, the recovered face images by current methods have the problems of over-smooth textures, ringing artifacts, and poor details. We consider the problem of face images deblurring as a semantic generation task. In this paper, we propose a generative adversarial network (GAN), which includes a perception-inspired blurry removal generator and a discriminator. The proposed generator reconstructs the latent deblurred image by a U-net based network that contains an enhancement module. Face images are highly structured, and thus can be served as a class-specific prior. Considering this, we propose a perceptual loss function to regularize the recovery of face images, which introduces more clear details and reduces the effects of artifacts. The proposed method has a robust capability of generating realistic face images with pleasant visual effects. Extensive experiments on both synthetic and real-world face images demonstrate that the proposed method is comparable with state-of-the-art methods.

The image compression-decompression process causes image quality degradations such as blocking and ringing artifacts. A convolutional neural network based on DCT domain is proposed to learn the mapping relationship between JPEG images and original images to reduce compression artifacts in this work. The convolutional neural network can exploit the prior knowledge of JPEG compression in DCT domain. The compression artifacts reduction network which is proposed in this work has three advantages. First, it can expand the receptive field and eliminate the discontinuity between each 8 × 8 block by overlapping extraction of patches. Second, it is based on the essence of distortion and can predict true DCT coefficients more accurately to reduce the loss of JPEG images. Third, it can obviously reduce the compression artifacts in JPEG images. Experiments on compressed images demonstrate that our approach achieves state-of-the-art performance in both the objective parameters and the subjective visual quality.

Medical image fusion involves combining multiple images of diverse modalities to acquire superior image quality while retaining the information of an individual image. The fusion process aids physicians to diagnose and assess the disease by increasing the visual information and clarity. Direct fusion methods often generate undesirable effects leading to distortion and poor contrast. To some extent, multi-scale decomposition (MSD) methods have achieved success in various image fusion problems. However, they suffer from ringing artifacts, due to the blur caused by strong edges in decomposition steps. Hence, to overcome these drawbacks, we propose an efficient and novel image fusion algorithm for constructing a fused image through total variation (TV-L1) model using an optimized adaptive weighting scheme. Our method focuses on transferring the prominent textural details of Magnetic Resonance Imaging (MRI) and visual details of Positron Emission Tomography (PET) by cartoon and texture decomposition. It consists of data fidelity term to force the cartoon component to remain close to original image and a regularization term to transfer gradients from original image to the fused image. The present work preserves structural and functional details with high contrast. Experiments were conducted using MRI and PET images collected from standard Brain Atlas Datasets. The qualitative and quantitative analysis of the work suggest that the proposed method achieves better quality and accuracy as compared with the available state-of-the-art techniques. Keywords: TV-L1, Particle swarm optimization, Image fusion, MRI, PET

Lossy compression brings artifacts into the compressed image and degrades the visual quality. In recent years, many compression artifacts removal methods based on convolutional neural network (CNN) have been developed with great success. However, these methods usually train a model based on one specific value or a small range of quality factors. Obviously, if the test image's quality factor does not match to the assumed value range, then degraded performance will be resulted. With this motivation and further consideration of practical usage, a highly robust compression artifacts removal network is proposed in this paper. Our proposed network is a single model approach that can be trained for handling a wide range of quality factors while consistently delivering superior or comparable image artifacts removal performance. To demonstrate, we focus on the JPEG compression with quality factors, ranging from 1 to 60. Note that a turnkey success of our proposed network lies in the novel utilization of the quantization tables as part of the training data. Furthermore, it has two branches in parallel---i.e., the restoration branch and the global branch. The former effectively removes the local artifacts, such as ringing artifacts removal. On the other hand, the latter extracts the global features of the entire image that provides highly instrumental image quality improvement, especially effective on dealing with the global artifacts, such as blocking, color shifting. Extensive experimental results performed on color and grayscale images have clearly demonstrated the effectiveness and efficacy of our proposed single-model approach on the removal of compression artifacts from the decoded image., Comment: Accepted for publication in the IEEE Transactions on Image Processing

By merging high-resolution panchromatic (PAN) images with low-resolution multispectral (MS) images, high-resolution MS images with complementary information can be obtained, i.e., pansharpening. Multiresolution analysis (MRA) methods have attracted widespread attention in the pansharpening field. The spatial detail information injected into MS images is extracted from PAN images by MRA tools. Since such methods often suffer from spatial distortion and ringing artifacts, a restoration algorithm based on blind deblurring and iterative back-projection (IBP) is proposed in this paper. First, a blind deblurring method based on the Tikhonov regular constraint model is used to estimate the blurring filter. Second, spatial details extracted from PAN images are modulated into MS images using a high-pass modulation (HPM) framework, and then fusion images are spatially enhanced based on the modulation results and blurring filter. Finally, the IBP technique is used to project the reconstruction error back to iteratively update and optimize the desired high-resolution images. Experiments are performed on data sets acquired by different satellites at full and reduced resolution, and eight state-of-the-art MRA-based pansharpening methods are used for validation. Compared to the enhanced back-projection (EBP) algorithm, the proposed restoration method is better in improving spectral and spatial quality of MRA-based pansharpening. The results indicate the effectiveness and superiority of the proposed method.

This paper presents a simultaneous autoregressive (SAR) analysis method to describe the unknown signal-to-noise ratio (SNR) and the texture feature of low-quality real video frames when the ground-truth images are not available. The real video images degraded by the factors, such as electronic noise, oversaturated pixels, motion blur, and compression artifacts, often result in poor motion registration estimation, which makes the performance of the existing video super-resolution (VSR) algorithms lower than expected. It is hard to estimate the SNR of the low-quality real frames without any prior knowledge. To solve this problem, we made a connection between SAR hyperparameters and the SNR of real images. The relationship expression of them was given in this paper. Using the proposed method, well-registered low-quality real video frames can be selected to decrease the root mean squared error (RMSE) of motion estimation of video frames for VSR reconstruction improvement. The anomalous low-quality frame images whose SAR hyperparameters values are inconsistent with others will be considered for removal. Synthetic experiments were designed to illustrate how the SAR hyperparameters values vary with the variation of synthetic parameters. In order to better illustrate the effectiveness of the proposed method, real low-quality videos captured under different conditions were tested under VSR reconstruction experiments. The VSR reconstruction results show that the results obtained using SAR prior analysis have sharper edges and fewer ringing artifacts than the original results. It indicates that the proposed method is helpful to obtain better results of motion registration estimation for low-quality real video images.

Gibbs-ringing is a common artifact in magnetic resonance imaging (MRI), which is mainly caused by the finite k-space sampling and the truncation of high frequency (HF) information at the sampling border. It is especially visible for imaging acquisitions at low resolution and can be typically suppressed by filtering at the expense of further loss of HF components. As a classic image restoration problem in MRI, Gibbs-ringing artifact suppression can be viewed as a typical ill-posed inverse problem of image generation in computer vision community, such as image super-resolution and inpainting. Inspired by this, the present work presents a novel method to suppress the Gibbs-ringing artifacts with knowledge transfer from natural images to MR images. The highly nonlinear relation between the artifact-degraded image and the corresponding artifact-free counterpart is modeled with a typical convolutional neural network (CNN), which is first trained with natural images and then fine-tuned with MR images. Unlike many other works, we use transfer learning between different types of images to deal with regression problems, rather than classification problems. The experimental results exhibit that there exists information sharing between natural images and MR images with regard to the same problem, and the knowledge learned from natural images can indeed improve the performance of regression models on MR images.

This paper develops a detail image signal enhancement that makes images perceived as clearer and more resolved and so is more effective for higher resolution displays. We observe that the local variant signal enhancement makes images more vivid, and the more revealed granular signals harmonically embedded on the local variant signals make images more resolved. Based on this observation, we develop a method that not only emphasizes the local variant signals by scaling up the frequency energy in accordance with human visual perception, but also strengths up the granular signals by embedding the alpha-rooting enhanced frequency components. The proposed energy scaling method emphasizes the detail signals in texture images and rarely boosts noisy signals in plain images. In addition, to avoid the local ringing artifact, the proposed method adjusts the enhancement direction to be parallel to the underlying image signal direction. It was verified through the subjective and objective quality evaluations that the developed method makes images perceived as clearer and highly resolved.

Photonics Integrated Interference Imaging System (PIIIS) is a recently developed far-filed imaging approach aiming at high imaging performance and compact structure. Several sampling lenslet arrays have been proposed to acquire higher imaging quality. These lens configurations provide low-frequency intensive but high-frequency sparse measurements of complex visibility. However, this kind of sampling will induce Gibbs-ringing artifacts in inverse Fourier images. In this article, entropy prior and smoothness prior are proposed to remove the ringing artifacts, and stabilized CLEAN algorithms based on those priors are used to reconstruct images. A simulation experiment using hierarchical multistage sampling lens array is implemented, and the results show that these algorithms can eliminate the ringing artifacts.

Purpose To develop a machine learning approach using convolutional neural network for reducing MRI Gibbs-ringing artifact. Theory and methods Gibbs-ringing artifact in MR images is caused by insufficient sampling of the high frequency data. Existing methods exploit smooth constraints to reduce intensity oscillations near sharp edges at the cost of blurring details. In this work, we developed a machine learning approach for removing the Gibbs-ringing artifact from MR images. The ringing artifact was extracted from the original image using a deep convolutional neural network and then subtracted from the original image to obtain the artifact-free image. Finally, its low-frequency k-space data were replaced with measured counterparts to enforce data fidelity further. We trained the convolutional neural network using 17,532 T2-weighted (T2W) normal brain images and evaluated its performance on T2W images of normal and tumor brains, diffusion-weighted brain images, and T2W knee images. Results The proposed method effectively removed the ringing artifact without noticeable smoothing in T2W and diffusion-weighted images. Quantitatively, images produced by the proposed method were closer to the fully-sampled reference images in terms of the root-mean-square error, peak signal-to-noise ratio, and structural similarity index, compared with current state-of-the-art methods. Conclusion The proposed method presents a novel and effective approach for Gibbs-ringing reduction in MRI. The convolutional neural network-based approach is simple, computationally efficient, and highly applicable in routine clinical MRI.

Compressed sensing-based reconstruction (CSR) is a new magnetic resonance (MR) image reconstruction method based on the compressed sensing (CS) technique. CSR suppresses ringing artifacts from truncated k-space sampling by estimating the high spatial frequency information required to support the acquired k-space data. CSR is intended to replace the existing zero-fill interpolation (ZIP) reconstruction. We investigated the usefulness of the CSR technique by obtaining sagittal T2-weighted images of the cervical spine and phantom images using CSR or ZIP. Our results indicated that the CSR technique reduces truncation artifacts compared to ZIP without prolonging the scan time or impairing image sharpness.

Blind image deblurring tries to restore a blurred image to a clear image without the blurring kernel known in advance, which is widely required in applications such as computer vision and medical image processing. With regard to this, the key issues here are to accurately estimate the blurring kernel for deconvolution of a blurred image, and avoid the ringing artifacts in the restored image, which are both related to high-quality detection of edge information in the blurred image. Though much endeavor has been made, it is still difficult to extract edge information well in blurred images and lacks investigation how edge information causes ringing artifacts. In this paper, we make a study on this and develop novel measures to optimize edge extraction and determine suitable width and weights for the extracted edges for reinforcing their use in deblurring, by which image deblurring can be improved with ringing artifacts considerably suppressed. Experimental results demonstrate our improvements over the existing methods.

H.265/HEVC (high efficiency video coding) adopts in-loop filters to reduce artifacts such as blocking artifacts, ringing artifacts, but those above-mentioned artifacts are still obvious in the case of large quantization step. This paper presents a novel in-loop filtering mechanism based on three-dimensional (3D) sub-bands in adaptive group of frames and convolutional neural network (CNN) models for further improvement of in-loop filtering capability. Firstly, the video frame sequence is adaptively grouped; then, a group of video frames is decomposed into frequency sub-bands by 3D wavelet transform; afterward, frequency sub-bands with different types are filtered by corresponding CNN model; finally, the group of video frames is synthesized by 3D wavelet inverse transform. We apply the nonlinear mapping ability of CNN into the modification of wavelet coefficients. The sub-bands of high frequency and low frequency are filtered by four offline CNN models with different directional characteristics trained with training data generated by 3D wavelet transform, respectively. The details of the high frequency part are enhanced, while the quality of low-pass image is promoted, so that those artifacts can be effectively alleviated. By means of comparing experiments, the subjective and objective results show that our proposed method has better filtering performance than the in-loop filtering mechanism in HM16.18; especially the video frame quality can be effectively improved in the case of large quantization step.

Motion blur exists in many computer vision tasks, including faces, texts, and low-illumination images etc. It has been proved that Dark Channel Prior (DCP) and Bright Channel Prior (BCP) can both help the image deblurring by enhancing the dark or bright channel pixels. However, the pixels between the dark channel pixels and the bright channel pixels are not taken into consideration, which limits the deblurring performance. A novel image channel is proposed in combination with dark channel and bright channel in this paper to consider the effects of the all types of pixels, namely, Michelson channel pixels. Secondly, as the image channels are built based on the series of image patches with different blur kernels, a new method is developed to estimate the blur kernel and can measure the similarity between neighbored kernels. Meanwhile, to perform accurate kernel estimation, the L0 regularization is applied into the algorithm framework. In the process of image deblurring, by enhancing the Michelson channels and retaining the other channels of the image, we can capture sharper image detail and eliminate the ringing artifacts of the recovered images. Massive experimental results demonstrate that the proposed method is more robust and outperforms the existing art-of-the-state of unsupervised image deblurring methods on both synthesized and natural images.

Converting a single low dynamic range (LDR) image into a high dynamic range (HDR) image, which is the so-called inverse tone mapping (ITM), is a challenging ill-posed problem since a lot of information is lost during compression and storage. Traditional ITM techniques mainly focus on high-quality LDR images without compression artifacts. However, in practice, LDR images are usually stored as a lossy compression format for the convenience of transmission, which will cause artifacts, i.e., blocking and ringing. Hence, these ITM methods suffer from severe performance drop for some real-world applications. In this paper, we propose a novel decomposition-based network to reconstruct HDR images from compressed LDR images; in other words, the proposed network can simultaneously remove blocking/ringing artifacts and recover high-quality HDR information. Considering compression artifacts mainly embedded in high frequency part, we thus decompose the input image into the high-frequency component (also known as detail layer) and low-frequency component (also known as a base layer). The detail layer mainly contains the information of texture, noise, and artifacts, while the base layer contains the information of structure and large object. Based on this, we design two subnetworks, i.e., detail layer recovery subnetwork and base layer recovery subnetwork, to restore the two parts separately. The detail layer recovery subnetwork is responsible for the artifacts removal with texture preserving, while the base layer recovery subnetwork is designed for tone expansion and overexposed/underexposed region restoration. Finally, in order to further reconstruct the serious overexposed regions, we adapt a merge subnetwork to fuse the result from the previous two subnetworks. The experimental results on compressed images demonstrate that the proposed method significantly outperforms other state-of-the-art methods.

Image priors have been successfully introduced to solve ill-posed problems, such as image denoising. In this paper, we propose a new denoising model for magnetic resonance images (MRIs) which employs the image low-rank and sparse gradient priors. First, we use a Gaussian mixture model (GMM) to guide the clustering of non-local self-similar patches by learning the structure of external noise-free MRI patches to help retain the low rank of the noisy MRI patch matrix. Second, we fit the heavy-tailed gradient of MRI with a hyper-Laplacian distribution to reduce ringing artifacts. Third, we adopt an alternating iterative algorithm. The experimental results show that our proposed algorithm outperforms many classical MRI denoising methods, such as unbiased nonlocal means (UNLM) filtering and block-matching and 3D (BM3D) filtering in both visual and numerical results.

A computational light-field display system is designed to realize floating three-dimensional (3D) visual effect. To simulate the natural vision of 3D scene, a multi-rays computational encryption method is proposed to obtain the image which is displayed on the LCD. In the process of encrypting image, N×N light rays are evenly involved in optimization to obtain the value of a pixel. The quality of reconstructed 3D image is correlated to N. As the value of N increases, the sharpness is improved and the ringing artifacts are eliminated. Here, the proposed computational light-field display system based on holographic functional screen (HFS) can provide floating 3D visual effect to observers with high quality. The constructed 3D image has continuously varying viewpoints with 40° viewing angle in horizontal and vertical directions. The displayed depth is more man 20 cm, and the center of the 3D object is on the surface of HFS.

In this paper, we propose high quality image debluring by using discrete cosine transform (DCT) with less computational complexity. Recently, Convolutional Neural Network (CNN) and Generative Adversarial Network (GAN) based algorithms have been proposed for image deblurring. Moreover, multi-scale architecture of CNN restores blurred image cleary and suppresses more ringing artifacts or block noise, but it takes much time to process. To solve these problems, we propose a method that preserves texture and suppresses ringing artifacts in the restored image without multi-scale architecture using DCT based loss named “DeblurDCTGAN.”. It compares frequency domain of the images made from deblurred image and ground truth image by using DCT. Hereby, DeblurDCTGAN can reduce block noise or ringing artifacts while maintaining deblurring performance. Our experimental results show that DeblurDCTGAN gets the highest performances on both PSNR and SSIM comparing with other conventional methods in GoPro, DVD, NFS and HIDE test Dataset. Also, the running time per pair of DeblurDCTGAN is faster than others.

Images acquired with standard digital cameras have Bayer patterns and suffer from lens blur. A demosaicking step is implemented in every digital camera, yet blur often remains unattended due to computational cost and instability of deblur-ring algorithms. Linear methods, which are computationally less demanding, produce ringing artifacts in deblurred images. Complex non-linear deblurring methods avoid artifacts, however their complexity imply offline application after camera demosaicking, which leads to sub-optimal performance. In this work, we propose a joint demosaicking deblurring and deringing network with a light-weight architecture inspired by the alternating direction method of multipliers. The proposed network has a transparent and clear interpretation compared to other black-box data driven approaches. We experimentally validate its superiority over state-of-the-art demosaicking methods with offline deblurring.

MRI images are visually inspected by domain experts for the analysis and quantification of the tumorous tissues. Due to the large volumetric data, manual reporting on the images is subjective, cumbersome, and error prone. To address these problems, automatic image analysis tools are employed for tumor segmentation and other subsequent statistical analysis. However, prior to the tumor analysis and quantification, an important challenge lies in the pre-processing. In the present study, permutations of different pre-processing methods are comprehensively investigated. In particular, the study focused on Gibbs ringing artifact removal, bias field correction, intensity normalization, and adaptive histogram equalization (AHE). The pre-processed MRI data is then passed onto 3D U-Net for automatic segmentation of brain tumors. The segmentation results demonstrated the best performance with the combination of two techniques, i.e., Gibbs ringing artifact removal and bias-field correction. The proposed technique achieved mean dice score metrics of 0.91, 0.86, and 0.70 for the whole tumor, tumor core, and enhancing tumor, respectively. The testing mean dice scores achieved by the system are 0.90, 0.83, and 0.71 for the whole tumor, core tumor, and enhancing tumor, respectively. The novelty of this work concerns a robust pre-processing sequence for improving the segmentation accuracy of MR images. The proposed method overcame the testing dice scores of the state-of-the-art methods. The results are benchmarked with the existing techniques used in the Brain Tumor Segmentation Challenge (BraTS) 2018 challenge. 2021 by the authors. Licensee MDPI, Basel, Switzerland. Funding: The authors also thank the Higher Education Commission (HEC), Pakistan, for funding this research under the Artificial Intelligence in Healthcare, IIPL, National Center of Artificial Intelligence (NCAI) and partial funding by HEC-SRGP research grant. Scopus

Natural images inevitably suffer from spatially variant blur caused by the relative motion between a camera and objects. We present an effective and efficient patch-wise edge-enhanced image regularization and a robust kernel similarity constraint to perform an accurate kernel estimation from coarse-to-fine iterations. The proposed adaptive regularization introduces a gradient magnitude penalty function into total variation to preserve and enhance salient edges while smoothing out harmful subtle structures. In addition, the similarity constraint is engaged in each patch without camera rotation effects, ensuring that the erroneous kernels can be identified by measuring the similarity among the kernels of neighbor patches and be replaced with the well-estimated ones. After obtaining accurate kernels, numerous nonblind deblurring methods can be applied to restore an image. Numerical experiments demonstrate that the proposed algorithm performs favorably without ringing artifacts and possesses high processing efficiency for natural nonuniform blurred images.

Limited-angle data, such as data obtained from a dual-panel breast-PET scanner, result in substantial image blur in directions coinciding with the missing cone of the image spectrum. On systems with time-of-flight (TOF) capabilities, this blur is reduced as given by the TOF uncertainty, with the image spectrum being correspondingly expanded into the missing spectral cone. Modeling of the TOF uncertainty in the reconstruction is expected to deconvolve this residual TOF blurring. We have however observed that, as a tradeoff, this TOF de-blurring process also introduces ringing artifacts at the edges, analogous to the edge effects observed with line-of-response (LOR) resolution modeling, which attempts to deconvolve the blur due to detector resolution effects. However, in the former case, the ringing artifacts are much wider due to the spatial extent of the TOF uncertainty as compared to the width of the typical LOR resolution blur. We illustrate and investigate the effects of using matched, as well as under-modeled and over-modeled, TOF kernels on edge artifacts in reconstruction from limited-angle data, and compare them with TOF reconstructions of complete data. Although for the conventional data with full angular coverage the reconstruction is fairly insensitive to the exact size of the TOF kernel and TOF modeling does not produce ringing artifacts, it is not the case for the limited-angle data. We show that it is important to use some form of regularization of the TOF uncertainty deconvolution process within the reconstruction of the limited-angle data, such as decreasing the TOF kernel size.

In video compression standards, in-loop filtering plays an important role in alleviating blocking, blurring and ringing artifacts caused by lossy compression, which enhances visual quality and benefits coding efficiency. The boom of neural network applications in super-resolution and image restoration brings insights into solutions of in-loop filtering in video codecs. In this paper, we design an asymmetric convolutional residual network (ACRN) for in-loop filtering in the state-of-the-art AV1 codec. With the asymmetric convolutional blocks, directional features can be extracted to restore textures and improve quality. The cascading structure of wide-activated residual blocks with pruned dense connections enables reflecting hierarchical coding unit (CU) partition characteristics of video coding without losing overall details. Experiments show that the proposed lightweight ACRN can bring up to 12.78% coding efficiency improvement in intra coding of AV1.

Pansharpening (PS) is a process used to generate high-resolution multispectral (MS) images from high-spatial-resolution panchromatic (PAN) and high-spectral-resolution multispectral images. In this paper, we propose a method for pansharpening by focusing on a compressed sensing (CS) technique. The spectral reproducibility of the CS technique is high due to its image reproducibility, but the reproduced image is blurry. Although methods of complementing this incomplete reproduction have been proposed, it is known that the existing method may cause ringing artifacts. On the other hand, component substitution is another technique used for pansharpening. It is expected that the spatial resolution of the images generated by this technique will be as high as that of the high-resolution PAN image, because the technique uses the corrected intensity calculated from the PAN image. Based on these facts, the proposed method fuses the intensity obtained by the component substitution method and the intensity obtained by the CS technique to move the spatial resolution of the reproduced image close to that of the PAN image while reducing the spectral distortion. Experimental results showed that the proposed method can reduce spectral distortion and maintain spatial resolution better than the existing methods.

The contrast and color fidelity of aerial images are usually seriously weakened and many features are covered because of atmospheric scattering and other factors. By using global features, low contrast images can be improved globally and the enhanced images may have little noise and ringing artifacts, but overexposure or underexposure may occur on some parts. By using local features, the details appear better, but it may lead to noise and ringing artifacts when the contrast gain is too large. In the paper, a new contrast enhancement method with adaptive gamma correction based on the weighting of global and local gray-scale mean is proposed. The adaptive gamma parameter which is obtained by incorporating the global and local gray-scale mean into the weighting distribution, is used to correct the gray value of each pixel in the image. Aerial images taken by DJI unmanned aerial vehicle with Inspire 2 at an altitude of 500 meters have been processed in the proposed method. Experimental results indicated that the proposed algorithm performs even better than the current mainstream methods in contrast enhancement for low visibility aerial images.

A method of estimating point spread function is designed in this paper. This PSF is applied in complete blind deconvolution for the restoration of defocus blurred images. The ringing artifact which is produced in the procession of image restoration needs to be suppressed. By using histogram to judge the type of random noise in the image, non local means filtering is chosen to denoise. Considering the long running time of the NLM filtering, only the wavelet low frequency coefficients are denoised with it. MATLAB simulations show that the designed PSF and this method have better effect of restoration and shorter running time.

The paper proposes an improvement of the grid warping algorithm for solving the edge sharpening problem. The idea of the grid warping is to transform the neighborhood of the edges in order to make the edge transient area thinner. This approach does not amplify the noise and does not introduce ringing artifact. The idea of the improvement is to analyze the curvature of the gradient field at edge point and adjust warping vectors.

We proposed a single image motion deblurring method with reduced ringing effects using variational Bayesian estimation.We propose a method to reduce ringing for image restoration using cyclic boundaries.Our algorithm can effectively suppress ringing artifacts and eliminate the influence of noise while protecting edge and detail information. Image degradation commonly occurs when camera translation or irregular movement causes motion blur. In this paper, we remove the effects of unknown camera shake from an image using variational Bayesian estimation and the statistical characteristics of natural image gradients. We then propose a method to reduce ringing effects for image restoration that uses cyclic boundaries. Finally, we use the Richardson-Lucy algorithm to restore an image. Experimental results demonstrate that the blind restoration algorithm that is proposed in this paper can effectively remove image blurring caused by camera shake. Our algorithm can effectively suppress ringing artifacts and eliminate the effects of noise while protecting both edge and detail information. In general, the proposed motion deblurring method is effective in reducing both noise and ringing effects to enhance the quality of motion-blurred image restoration, and we obtained restored images that were clearer and more reliable.

Limited angle data, such as data obtained from a dual panel Breast-PET system, result in substantial image blur in the directions coinciding with the missing cone of the image spectrum. On systems with time-of-flight (TOF) capabilities, this blur is reduced in length as given by the TOF uncertainty, with the image spectral data correspondingly being expanded into the missing spectral cone. Modeling of the TOF uncertainty in the reconstruction is expected in turn to deconvolve the TOF blurring, at least for ideal noiseless data. We have however observed that, as a tradeoff, the TOF de-blurring process, when reconstructing limited angle data, also introduces ringing artifacts at the de-blurred objects edges, analogous to the edge effects observed with resolution modeling of the point-spread function (PSF) within image reconstruction which attempts to deconvolve the blur due to spatial resolution degradation effects. However, in the former case, the ringing artifacts are much wider due to the size of the TOF uncertainty kernel being considerably larger than the size of typical LOR resolution kernels. In this work, we illustrate and investigate the effects of using matched as well as under- and over-modeled TOF resolution kernels on edge artifacts in PET reconstruction from limited angle data, and compare those with the effects of TOF reconstruction of complete data from a conventional full-ring scanner.

Incorporating image-domain point spread function (PSF) modeling inside statistical reconstructions in emission tomography is capable of improving spatial resolution and tumor contrasts by modeling resolution degrading phenomena such as positron range, inter-crystal scatter and photon non-colinearity. However, such improvements do not necessarily translate into superior performance in clinical tasks and PSF modeling may introduce Gibbs-like ringing artifacts along edges due to the attempted recovery of high frequency information that is lost or heavily attenuated during data acquisition. In previous work we had proposed an approach for quantifying the visible portions of ringing artifacts and showed that for low-count datasets, overmodeling PSF kernels to a certain extent could help improve image quality while keeping visible ringing at a minimum. In this work, we develop a rule for automatically choosing the optimal PSF kernel width based on data quality and evaluate our method using a real patient study. We analyzed the performance of ROI quantification for the proposed PSF modeling approach and compared the results with standard, fixed-width PSF modeling using list-mode ordered subsets expectation maximization (OSEM) reconstructions. We acquired five whole-body patient scans with both real and inserted lesions. The contrast recovery coefficient (CRC) for inserted lesions or the mean lesion standard uptake value (SUV) for real lesions versus liver background variability was used for quantifying patient data. The results show that the proposed count-level-adaptive PSF modeling approach can achieve better quantification performance compared to standard PSF modeling without enhancing ringing artifacts.

The autoregressive (AR) model is widely used in image interpolations. Traditional AR models consider utilizing the dependence between pixels to model the image signal. However, they ignore the valuable patch-level information for image modeling. In this paper, we propose to integrate both the pixel-level and patch-level information to depict the relationship between high-resolution and low-resolution pixels and obtain better image interpolation results. In particular, we propose an isophote-constrained AR (ICAR) model to perform AR-flavored interpolation within an identified joint stable region and further develop an AR interpolation with an adaptive window extension. Considering the smoothness along the isophote curve, the ICAR model searches only several successive similar patches along the isophote curve over a large region to construct an adaptive window. These overlapped patches, representing the patch-level structure similarity, are used to construct a joint AR model. To better characterize the piecewise stationarity and determine whether a pixel is suitable for AR estimation, we further propose pixel-level and patch-level similarity metrics and embed them into the ICAR model, introducing a weighted ICAR model. Comprehensive experiments demonstrate that our method can effectively reconstruct the edge structures and suppress jaggy or ringing artifacts. In the objective quality evaluation, our method achieves the best results in terms of both peak signal-to-noise ratio and structural similarity for both simple size doubling (two times) and for arbitrary scale enlargements.

Motion blur is a common problem in digital photography. In the dim light, a long exposure time is needed to acquire a satisfactory photograph, and if the camera shakes during exposure, a motion blur is captured. Image deblurring has become a crucial image-processing challenge, because of the increased popularity of handheld cameras. Traditional motion deblurring methods assume that the blur degradation is shift-invariant; therefore, the deblurring problem can be reduced to a deconvolution problem. Edge-specific motion deblurring sharpened the strong edges of the image and then used them to estimate the blur kernel. However, this also enhanced noise and narrow edges, which cause ambiguity and ringing artifacts. We propose a hybrid-based single image motion deblurring algorithm to solve these problems. First, we separated the blurred image into strong edge parts and smooth parts. We applied the improved patch-based sharpening method to enhance the strong edge for kernel estimation, but for the smooth part, we used the bilateral filter to remove the narrow edge and the noise for avoiding the generation of ringing artifacts. Experimental results show that the proposed method is efficient at deblurring for a variety of images and can produce images of a quality comparable to other state-of-the-art techniques.

In this paper, we present a simple yet effective image deblurring method to produce ringing-free deblurred images. Our work is inspired by the observation that large-scale deblurring ringing artifacts are measurable through a multi-resolution pyramid of low-pass filtering of the blurred-deblurred image pair. We propose to model such a quantification as a convex cost function and minimize it directly in the deblurring process in order to reduce ringing regardless of its cause. An efficient primal-dual algorithm is proposed as a solution to this optimization problem. Since the regularization is more biased toward ringing patterns, the details of the reconstructed image are prevented from over-smoothing. An inevitable source of ringing is sensor saturation which can be detected costlessly contrary to most other sources of ringing. However, dealing with the saturation effect in deblurring introduces a non-linear operator in optimization problem. In this paper, we also introduce a linear approximation as a solution to handling saturation in the proposed deblurring method. As a result of these steps, we significantly enhance the quality of the deblurred images. Experimental results and quantitative evaluations demonstrate that the proposed method performs favorably against state-of-the-art image deblurring methods.

Nonlocal image representation has been successfully used in many image-related inverse problems including denoising, deblurring and deblocking. However, most existing methods only consider the nonlocal self-similarity (NSS) prior of degraded observation image, and few methods use the NSS prior from natural images. In this paper we propose a novel method for image denoising via group sparsity residual constraint with external NSS prior (GSRC-ENSS). Different from the previous NSS prior-based denoising methods, two kinds of NSS prior (e.g., NSS priors of noisy image and natural images) are used for image denoising. In particular, to enhance the performance of image denoising, the group sparsity residual is proposed, and thus the problem of image denoising is translated into reducing the group sparsity residual. Because the groups contain a large amount of NSS information of natural images, to reduce the group sparsity residual, we obtain a good estimation of the group sparse coefficients of the original image by the external NSS prior based on Gaussian Mixture Model (GMM) learning, and the group sparse coefficients of noisy image are used to approximate the estimation. To combine these two NSS priors better, an effective iterative shrinkage algorithm is developed to solve the proposed GSRC-ENSS model. Experimental results demonstrate that the proposed GSRC-ENSS not only outperforms several state-of-the-art methods, but also delivers the best qualitative denoising results with finer details and less ringing artifacts.

The photonics integrated interference imaging system (PIIIS) is a recently developed far-field imaging approach aimed at compacting structures and acquiring high imaging performance. In this regard, to acquire higher imaging quality, several lens patterns have been developed, providing low-frequency intensive but high-frequency sparse measurements of the visibility function. However, sparse measurements at high frequency produce Gibbs-ringing artifacts in the image reconstruction, severely damaging the PIIIS imaging quality. In this study, a new data penalty for revised entropy is proposed for PIIIS, and a simple reconstruction algorithm using the Newton method is developed. A reconstruction experiment adopting the hierarchical multistage pattern is implemented on resolution board (USAF1951) targets with bright and black backgrounds. The outstanding improvement in the experimental results proves the feasibility of suppressing the ringing artifacts in the images reconstructed in PIIIS.

The modulation transfer function (MTF) is a well-established in imaging systems and has played a significant role in assessing the performance of information transfer in accordance with the spatial frequency. Exist methods for measuring MTF require a pinhole, or a slit camera, an edge phantom, etc. These depend on the imaging system and exposure conditions. To overcome these problems, we investigated the proposed patch-based MTF prediction framework using a deep-learning algorithm in radiography. This proposed method directly allows for the predicted MTF from a single image without the need for additional devices and work. We used 800 to 1500 projections for supervised learning and selected many patches, including the edge information from input image for predicting the MTF, for which we used the Kendall's rank correlation. The quantitative evaluation performed included an intensity profile and a root-mean square error. In addition, we implemented the nonblind deblurring process to verify its image performance using the predicted MTF. Our methods successfully predicted the MTF and restored them from the degraded image without such artifacts as the ringing artifact that can occur when using the incorrect blur kernel. Consequently, the simulation and experimental results indicate that the proposed framework is useful to predicting the accurate MTF and is effective in improving radiographic image quality.

This study aims to introduce a new compressed sensing averaging (CSA) technique for the reduction of blurring and/or ringing artifacts, depending on the k-space sampling ratio. A full k-space dataset and three randomly undersampled datasets were obtained for CSA images in a brain phantom and a healthy subject. An additional simulation was performed to assess the effect of the undersampling ratio on the images and the signal-to-noise ratios (SNRs). The image sharpness, spatial resolution, and contrast between tissues were analyzed and compared with other CSA techniques. Compared to CSA with multiple acquisition (CSAM) at 25%, 35%, and 45% undersampling, the reduction rates of the k-space lines of CSA with keyhole (CSAK) were 10%, 15%, and 22%, respectively, and the acquisition time was reduced by 16%, 23%, and 32%, respectively. In the simulation performed with a full sampling k-space dataset, the SNR decreased to 10.41, 9.80, and 8.86 in the white matter and 9.69, 9.35, and 8.46 in the gray matter, respectively. In addition, the ringing artifacts became substantially more predominant as the number of sampling lines decreased. The 50% modulation transfer functions were 0.38, 0.43, and 0.54 line pairs per millimeter for CSAM, CSAK with high-frequency sharing (CSAKS), and CSAK with high-frequency copying (CSAKC), respectively. In this study, we demonstrated that the smaller the sampling line, the more severe the ringing artifact, and that the CSAKC technique proposed to overcome the artifacts that occur when using CSA techniques did not generate artifacts, while it increased spatiotemporal resolution.

Integral imaging for image watermarking is attracting a great deal of interest as it provides high processing speed and data redundancy property. Traditional video watermarking algorithms are mostly based on discrete transform domains, such as the discrete cosine transform and discrete wavelet transform. Most of these algorithms generally provide one transform plane for watermarking. In this paper, we propose a video watermarking by employing three-dimensional (3-D) cellular automata (CA) transform and perfect-reconstruction integral imaging (PRII). Compared to the existing works, the main strength of this paper is to utilize a 3-D CA transform to produce multiple transform planes for watermark embedding, so that greatly improve the security; decompose watermark into a series of elemental images using integral imaging to enhance the robustness; and employ perfect-reconstruction filter into the iterative back-projection process thus resolving the ringing artifacts problems. Experimental results show that the proposed PRII-based watermarking method outperforms other similar watermarking methods and is robust to a wide range of attacks, e.g., Gaussian noise, salt and pepper noise, frame clipping, JPEG compression, and median filter attack.

In this paper, we have proposed a blind motion deblurring algorithm that comprises the estimation of the motion blur parameters (length and angle) in a modified cepstrum domain with a blind...

We propose a superresolution (SR) algorithm based on adaptive sparse representation via multiple dictionaries for images taken by unmanned aerial vehicles (UAVs). The SR attainable through the proposed algorithm can increase the precision of 3-D reconstruction from UAV images, enabling the production of high-resolution images for constructing high-frequency time series and for high-precision digital mapping in agriculture. The basic idea of the proposed method is to use a field server or ground-based camera to take training images and then construct multiple pairs of dictionaries based on selective sparse representations to reduce instability during the sparse coding process. The dictionaries are classified on the basis of the edge orientation into five clusters: 0, 45, 90, 135, and nondirection. The proposed method is expected to reduce blurring, blocking, and ringing artifacts especially in edge areas. We evaluated the proposed and previous methods using peak signal-to-noise ratio, structural similarity, feature similarity, and computation time. Our experimental results indicate that the proposed method clearly outperforms other state-of-the-art algorithms based on qualitative and quantitative analysis. In the end, we demonstrate the effectiveness of our proposed method to increase the precision of 3-D reconstruction from UAV images.

Purpose Single image super-resolution (SR) is highly desired in many fields but obtaining it is often technically limited in practice. The purpose of this study was to propose a simple, rapid and robust single image SR method in magnetic resonance (MR) imaging (MRI). Methods The idea is based on the mathematical formulation of the intrinsic link in k-space between a given (modulus) low-resolution (LR) image and the desired SR image. The method consists of two steps: 1) estimating the low-frequency k-space data of the desired SR image from a single LR image; 2) reconstructing the SR image using the estimated low-frequency and zero-filled high-frequency k-space data. The method was evaluated on digital phantom images, physical phantom MR images and real brain MR images, and compared with existing SR methods. Results The proposed SR method exhibited a good robustness by reaching a clearly higher PSNR (25.77dB) and SSIM (0.991) averaged over different noise levels in comparison with existing edge-guided nonlinear interpolation (EGNI) (PSNR=23.78dB, SSIM=0.983), zero-filling (ZF) (PSNR=24.09dB, SSIM=0.985) and total variation (TV) (PSNR=24.54dB, SSIM=0.987) methods while presenting the same order of computation time as the ZF method but being much faster than the EGNI or TV method. The average PSNR or SSIM over different slice images of the proposed method (PSNR=26.33 dB or SSIM=0.955) was also higher than the EGNI (PSNR=25.07dB or SSIM=0.952), ZF (PSNR=24.97dB or SSIM=0.950) and TV (PSNR=25.70dB or SSIM=0.953) methods, demonstrating its good robustness to variation in anatomical structure of the images. Meanwhile, the proposed method always produced less ringing artifacts than the ZF method, gave a clearer image than the EGNI method, and did not exhibit any blocking effect presented in the TV method. In addition, the proposed method yielded the highest spatial consistency in the inter-slice dimension among the four methods. Conclusions This study proposed a fast, robust and efficient single image SR method with high spatial consistency in the inter-slice dimension for clinical MR images by estimating the low-frequency k-space data of the desired SR image from a single spatial modulus LR image.

The development of image sharpening algorithms is currently one of the most important problems in image processing. This paper presents a parallel implementation of a pixel grid warping algorithm for image sharpening. To sharpen image edges, the proposed algorithm moves pixels in the edge neighborhood towards edge centers rather than changing pixel values directly. This approach does not increase noise and does not cause ringing artifacts.

Image and video compression algorithms may yield visual annoying artifacts for limited bit rates. The quality of compressed video signals may be improved by reducing blocking and ringing artifacts. For removing the blocking artifact, a quality measure of a block-based coded image is used to decide filtering modes. Based on filtering modes, the images are segmented into three classes and a specific deblocking filter is applied to each class. An adaptive method for extending the bilateral filter to a trilateral filter is introduced for removing the ringing artifacts. The spread parameters of the trilateral filter are selected adaptively using texture and edge mapping. The analysis of objective and subjective experimental results shows that the proposed algorithm is robustly successful in artifact reduction of low-bit rate H.264 video sequences.

Iterative methods are typically utilized for blind image restoration (BIR); however, they are relatively slow, uncertain, and occasionally ill-behaved. This study presents a non-iterative algorithm to estimate the parameters of point spread functions (PSFs), particularly, Class G. We propose a curve model to approximate the normalized spectrum amplitude of the original image in accordance with the decay law of the natural image spectrum. The blur PSF is estimated by comparing the original image spectrum with the degraded one. Then, the image is restored by applying the estimated PSF and the Wiener filter. Experimental results demonstrate that the proposed algorithm can obtain a more accurate PSF and reduce ringing artifacts compared with the existing algorithms. The quality of the restored images is enhanced significantly.

The purpose of this study is to improve direct phosphorus (31P) MR imaging. Therefore, 3D density-adapted radially-sampled balanced steady-state free precession (bSSFP) sequences were developed and an iterative approach exploiting additional anatomical information from hydrogen (1H) data was evaluated. Three healthy volunteers were examined at B0=7T in order to obtain the spatial distribution of the phosphocreatine (PCr) intensities in the human calf muscle with a nominal isotropic resolution of 10mm in an acquisition time of 10min. Three different bSSFP gradient schemes were investigated. The highest signal-to-noise ratio (SNR) was obtained for a scheme with two point-reflected density-adapted gradients. Furthermore, the conventional reconstruction based on a gridding algorithm was compared to an iterative method using an 1H MRI constraint in terms of a segmented binary mask, which comprises prior knowledge. The parameters of the iterative approach were optimized and evaluated by simulations featuring 31P MRI parameters. Thereby, partial volume effects as well as Gibbs ringing artifacts could be reduced. In conclusion, the iterative reconstruction of 31P bSSFP data using an 1H MRI constraint is appropriate for investigating regions where sharp tissue boundaries occur and leads to images that represent the real PCr distributions better than conventionally reconstructed images.