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Single Image Super-Resolution (SISR) has seen significant advancements with the advent of deep learning techniques. However, many existing approaches face challenges such as high computational costs, poor generalization to unseen data and dependence on large paired datasets. This paper proposes a novel, lightweight Parallel Super-Resolution Convolutional Neural Network (PSRCNN) designed to address these limitations. PSRCNN leverages parallel feature extraction, a transposed convolutional upsampling layer and an efficient feature fusion strategy to balance performance and efficiency. Rigorous evaluations on established benchmark datasets demonstrate that PSRCNN achieves competitive performance, particularly in terms of the Structural Similarity Index (SSIM), a metric closely aligned with human visual perception. Moreover, the model showcases a significant advantage in computational efficiency, requiring fewer parameters than many recent Super-Resolution (SR) methods. PSRCNN presents a promising approach to SISR, demonstrating the potential of parallel CNN architectures for image SR tasks, as validated by ablation studies confirming the effectiveness of this design in enhancing image reconstruction quality. This approach is open to further enhancement. [ABSTRACT FROM AUTHOR]

We consider a shape optimization based method for finding the best interpolation data in the compression of images with noise. The aim is to reconstruct missing regions by means of minimizing a data fitting term in an L p -norm, for 1 ⩽ p < + ∞, between original images and their reconstructed counterparts using linear diffusion PDE-based inpainting. Reformulating the problem as a constrained optimization over sets (shapes), we derive the topological asymptotic expansion of the considered shape functionals with respect to the insertion of small ball (a single pixel) using the adjoint method. Based on the achieved distributed topological shape derivatives, we propose a numerical approach to determine the optimal set and present numerical experiments showing the efficiency of our method. Numerical computations are presented that confirm the usefulness of our theoretical findings for PDE-based image compression. [ABSTRACT FROM AUTHOR]

This paper proposes a technique for detecting impact-induced surface and subsurface fractures in Glass Fibre Reinforced Composites (GFRPs) and hybrid composite materials. Various categories of composite samples, namely Unidirectional GFRP, Bidirectional GFRP, Woven mat GFRP and Carbon-flax hybrid composites are fabricated by the hand-layup process, and cracks are induced in the samples by three-point flexural bending test using a Universal Testing Machine. Near-field microwave Non-Destructive Testing (NDT) is employed in detecting the above cracks by recording the S11 parameter from the microwave transceiver probe throughout the sample. For image rendering the resulting outputs are passed as a 2-dimensional array to the image-rendering algorithms, namely Iterative Curve-Based Interpolation (ICBI), Improved New Edge-Directed Interpolation (INEDI) and the Lanczos algorithm. The most suitable algorithm to identify the clear picture and size of crack is identified, and the effectiveness of Microwave NDT for these composites is analysed. [ABSTRACT FROM AUTHOR]

Ophthalmological imaging is widely used for the diagnosis of eye diseases. It is used to acquire high-quality images of either the cornea or the retina. Unfortunately, the acquired images have limited quality and resolution. The issue of visual quality and resolution enhancement of corneal images is the main concern in this paper. A framework is introduced for quality enhancement of corneal images with both histogram equalization and interpolation. Two models are presented in this framework. The first model comprises histogram equalization first and then interpolation, and the second one comprises interpolation first and then histogram equalization. Two types of image interpolation are considered: polynomial interpolation and inverse interpolation. These models are compared to achieve the best quality of corneal images. This best quality of corneal images is obtained with regularized interpolation first and then histogram equalization. The rationale behind this conclusion is that the utilization of regularization theory in interpolation presents smooth areas and reinforced edges. [ABSTRACT FROM AUTHOR]

Image super-resolution (SR) is a classic problem of image processing. This paper proposes a self-learning interpolation method (SLIM) based on a single image by combining grid feature mapping with binary decision tree, which is not only transparent as the interpolation-based methods, but also achieves comparable performance as the learning-based methods. Firstly, it downsamples the given image I LR to obtain its low–low-resolution image I LLR , which is used to obtain sample data for the self-learning interpolation algorithm for enlarging I LLR to get I LR . Secondly, it provides a structural feature classification method to divide all of the samples into several groups, such that each class of I LLR is mapped to a matrix of coefficients for calculating the values of the pixels of I LR . The image I LR is approximated by executing the decision tree to refine the corresponding mapping matrix. Finally, the resulting high-resolution image I HR is obtained from the given image I LR by using the mapping matrixes. Experimental results show that SLIM achieves more smooth edges and better details on subjective vision than prevailing SR methods, and it is a transparent one but achieves comparable performances on PSNR and SSIM with the learning-based methods, while it outperforms the interpolation-based methods. It means that SLIM is both transparent and efficient and has much better subjective vision than other SR methods. [ABSTRACT FROM AUTHOR]

3D reconstruction technology is gradually applied to underwater scenes, which has become a crucial research direction for human ocean exploration and exploitation. However, due to the complexity of the underwater environment, the number of high-quality underwater images acquired by underwater robots is limited and cannot meet the requirements of 3D reconstruction. Therefore, this paper proposes an adaptive 3D reconstruction algorithm for underwater targets. We apply the frame interpolation technique to underwater 3D reconstruction, an unprecedented technical attempt. In this paper, we design a single-stage large-angle span underwater image interpolation model, which has an excellent enhancement effect on degraded underwater 2D images compared with other methods. Current methods make it challenging to balance the relationship between feature information acquisition and underwater image quality improvement. In this paper, an optimized cascaded feature pyramid scheme and an adaptive bidirectional optical flow estimation algorithm based on underwater NRIQA metrics are proposed and applied to the proposed model to solve the above problems. The intermediate image output from the model improves the image quality and retains the detailed information. Experiments show that the method proposed in this paper outperforms other methods when dealing with several typical degradation types of underwater images. In underwater 3D reconstruction, the intermediate image generated by the model is used as input instead of the degraded image to obtain a denser 3D point cloud and better visualization. Our method is instructive to the problem of acquiring underwater high-quality target images and underwater 3D reconstruction. [ABSTRACT FROM AUTHOR]

Recently, learning-based image interpolation methods have gained significant popularity in the field of surveillance image processing due to their promising results. Notably, deep neural networks have shown considerable improvements in image super-resolution. To enhance the performance of image interpolation for surveillance images, researchers often employ deep convolutional neural Networks. However, merely increasing the network depth may not lead to substantial improvements and could even introduce new training-related challenges, necessitating novel training approaches. Therefore, in this proposed work, a new deep learning-based model is developed specifically tailored for effective surveillance image interpolation. The approach begins with the Optimized Recursive Least Square Adaptive Filter (ORLSAF) technique for image filtering. This step involves calculating the error signal and estimating weight factors to generate noise-free images. To reconstruct the interpolated surveillance images, the innovative Multi-Variate Dense Fusion Network (MVDFN) methodology is utilized, which incorporates feature fusion, augmentation, and loss regularization processes. Particularly, the loss factor is optimally calculated using the Hybrid Butterfly Optimization (HBO) algorithm. To evaluate the performance of the proposed technique, extensive experiments are conducted using a benchmarking dataset commonly employed in surveillance image processing. The evaluation metrics used include Peak Signal-to-Noise Ratio (PSNR), Structural Similarity Index (SSIM), loss factor, and normalized similarity index. Overall, this research aims to advance surveillance image interpolation using a novel deep learning-based approach, combining ORLSAF, MVDFN, and the HBO algorithm to achieve superior results compared to existing methods. The potential impact of this work includes enhancing the quality and clarity of surveillance images, contributing to improved surveillance systems and applications. [ABSTRACT FROM AUTHOR]

In this paper, image fusion is applied on both magnetic resonance (MR) and computed tomography (CT) images to generate a single image with more details. The objective is to help specialists to detect brain tumors, accurately. In addition, this paper introduces a comparative study of various types of image fusion and interpolation techniques to help for better diagnosis of brain tumors. Then, threshold segmentation is used to detect the brain tumor region. Finally, a morphological operation is performed to shape the tumor region, regularly. The performance of the brain tumor segmentation process with all image fusion and interpolation techniques is evaluated, revealing high accuracy. [ABSTRACT FROM AUTHOR]

Accurate unmanned aerial vehicle (UAV) trajectory tracking is crucial for the successful execution of UAV missions. Traditional global positioning system (GPS) methods face limitations in complex environments, and visual observation becomes challenging with distance and in low-light conditions. To address this challenge, we propose a comprehensive framework for UAV trajectory verification, integrating a range-based ultra-wideband (UWB) positioning system and advanced image processing technologies. Our key contribution is the development of the Spatial Trajectory Enhanced Attention Mechanism (STEAM), a novel attention module specifically designed for analyzing and classifying UAV trajectory patterns. This system enables real-time UAV trajectory tracking and classification, facilitating swift and accurate assessment of adherence to predefined optimal trajectories. Another major contribution of our work is the integration of a UWB system for precise UAV location tracking, complemented by our advanced image processing approach that includes a deep neural network (DNN) for interpolating missing data from images, thereby significantly enhancing the model's ability to detect abnormal maneuvers. Our experimental results demonstrate the effectiveness of the proposed framework in UAV trajectory tracking, showcasing its robust performance irrespective of raw data quality. Furthermore, we validate the framework's performance using a lightweight learning model, emphasizing both its computational efficiency and exceptional classification accuracy. [ABSTRACT FROM AUTHOR]

Digital imaging is used in variety of applications. Together with the improvements in artificial intelligence and its sub-fields, improving computer vision methods to address inter- and multi-disciplinary problems is possible. Especially in medical applications, there are significant improvements related to imaging in the last decades. Digital image interpolation is a key operation in digital image processing where there are no sufficient samples during the acquisition process. Using the available samples in hand, digital interpolation techniques are predicting the missing samples. The paper addresses the problem of digital image interpolation and proposes a novel algorithm using multiplicative calculus. The main contribution of the paper is the application of multiplicative Lagrange interpolation to accomplish image interpolation task. The proposed method is tested on several datasets, and the results are comparable to the state-of-the-art methods. The paper presents encouraging results to the literature, and the proposed method is open for further improvements. [ABSTRACT FROM AUTHOR]

Increasing data hiding capacity and reducing cover image distortions are the main objectives of any data hiding technique. Moreover, some applications require the reversibility of the data hiding technique so that the original cover image is exactly recovered in the extraction step. Interpolation-based data hiding techniques have the advantage of providing high data hiding capacity. However, they suffer two drawbacks: they are not truly reversible and introduce high distortions to the cover image. This paper presents a new interpolation-based data hiding technique that is adaptive, truly reversible, vastly reduces the cover image distortion, and takes the sensitivity of the Human Visual System (HVS) into consideration. Unlike other interpolation techniques, our proposed technique eliminates the down-scaling and expansion steps in typical interpolation-based techniques. Instead, it embeds data into the original cover image. It uses a simple, efficient interpolation algorithm to take the sensitivity of the HVS into account by limiting the distortions in smooth regions of the cover image where the HVS is more sensitive to distortions. Using dual cover images and an improved interpolation algorithm achieves reversibility, vastly reduces cover image distortion, and achieves high data hiding capacity. The downscaling and expansion step in typical interpolation-based data hiding techniques results in poor quality cover images with a peak signal-to-noise ratio (PSNR) in the neighborhood of 25 dB. The proposed technique eliminates this step and produces high-quality stego images with 42dBs minimum average PSNR values. [ABSTRACT FROM AUTHOR]

Due to the recent rapid development of the ICT (Information and Communications Technology) field, the industrial sector is also experiencing rapid informatization. As a result, malware targeting information leakage and financial gain are increasingly found within IIoT (the Industrial Internet of Things). Moreover, the number of malware variants is rapidly increasing. Therefore, there is a pressing need for a safe and preemptive malware detection method capable of responding to these rapid changes. The existing malware detection method relies on specific byte sequence inclusion in a binary file. However, this method faces challenges in impacting the system or detecting variant malware. In this paper, we propose a data augmentation method based on an adversarial generative neural network to maintain a secure system and acquire necessary learning data. Specifically, we introduce a digital twin environment to safeguard systems and data. The proposed system creates fixed-size images from malware binaries in the virtual environment of the digital twin. Additionally, it generates new malware through an adversarial generative neural network. The image information produced in this manner is then employed for malware detection through deep learning. As a result, the detection performance, in preparation for the emergence of new malware, demonstrated high accuracy, exceeding 97%. [ABSTRACT FROM AUTHOR]

This paper proposes a novel method for the image interpolation problem based on two-dimensional discrete wavelet transform (DWT) with the edge preserving approach. The purpose of this method is to consider two contrasting issues of over-smoothing and creation of spurious edges at the same time, and offer a novel solution based on statistical dependencies of image sub-bands, and noise behavior. The offered method has a multi-faceted approach for the problem; by sub-band coding, it handles each 2D-DWT image sub-band with a different solution. For LH and HL sub-bands, two algorithms work together in order to preserve regularity. Area_Check algorithm is a four-phase edge-preserving algorithm that aims to recognize and interpolate separating lines of environments and edgy regions in the best possible way. On the other hand, RLS_AVG algorithm interpolates smooth surfaces of the image by keeping the regularity of the image without over-smoothing. In this regard, the offered algorithm has a great power to counter jaggies and annoying artifacts. In the end, in order to demonstrate the capability, and performance of the proposed method, the final results in various metrics are compared with the results of the most famous and the newest image interpolation methods. [ABSTRACT FROM AUTHOR]

This study proposes a large capacity, reversible, and separable algorithm based on adaptive embedding to address the issues plaguing reversible data hiding in the encrypted images (RDH-EI) algorithms, such as reversibility, low embedding rate, and incomplete separation. An improved Arnold and chaos-based image encryption algorithm is proposed in the image encryption stage, permitting the original image several times using the Arnold matrix and performing pixel diffusion using the generated chaotic sequence. Then, the encrypted image is interpolated with an improved image interpolation algorithm to create the cover image to be embedded with the secret data. The difference between the secret data to be embedded and the maximum value of $n$ -bit data is first calculated in the data embedding stage. Then, an adaptive data embedding method is proposed to embed the secret data into the interpolated pixels, based on the difference and the size of the secret data. Experimental results show that the proposed algorithm is fully reversible, has no additional data and no data overflow, and is separable with all uncorrelated keys. The proposed algorithm has higher image quality at the same embedding rate than others. It is also resistant to histogram and regular singular (RS) steganalysis.

Traditional image steganography techniques complete the steganography process by embedding secret information into cover images, but steganalysis tools can easily detect detectable pixel changes that lead to the leakage of confidential information. The use of a generative adversarial network (GAN) makes it possible to embed information using a combination of information and noise in generating images to achieve steganography. However, this approach is usually accompanied by issues such as poor image quality and low steganography capacity. To address these challenges, we propose a steganography model based on a novel information-driven generative adversarial network (IDGAN), which fuses a GAN, attention mechanisms, and image interpolation techniques. We introduced an attention mechanism on top of the original GAN model to improve image accuracy. In the generation model, we replaced some transposed convolution operations with image interpolation for better quality of dense images. In contrast to traditional steganographic methods, the IDGAN generates images containing confidential information without using cover images and utilizes GANs for information embedding, thus having better anti-detection capability. Moreover, the IDGAN uses an attention mechanism to improve the image details and clarity and optimizes the steganography effect through an image interpolation algorithm. Experimental results demonstrate that the IDGAN achieves an accuracy of 99.4%, 95.4%, 93.2%, and 100% on the MNIST, Intel Image Classification, Flowers, and Face datasets, respectively, with an embedding rate of 0.17 bpp. The model effectively protects confidential information while maintaining high image quality. [ABSTRACT FROM AUTHOR]

Copyright of Journal of Remote Sensing is the property of Editorial Office of Journal of Remote Sensing & Science Publishing Co. and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

When information is exchanged through open communication networks, there is a possibility of third-party interception. Various methods of data protection have been developed and applied to eliminate this flaw. In this work, a task of developing a new reversible steganographic method of concealing information based on interpolation of an image is set and solved. The developed algorithm has a higher payload of secret information while preserving the high visual quality of the stego image. Results of the pilot studies confirm this and are presented in this article.

Accurate unmanned aerial vehicle (UAV) trajectory tracking is crucial for the successful execution of UAV missions. Traditional global positioning system (GPS) methods face limitations in complex environments, and visual observation becomes challenging with distance and in low-light conditions. To address this challenge, we propose a comprehensive framework for UAV trajectory verification, integrating a range-based ultra-wideband (UWB) positioning system and advanced image processing technologies. Our key contribution is the development of the Spatial Trajectory Enhanced Attention Mechanism (STEAM), a novel attention module specifically designed for analyzing and classifying UAV trajectory patterns. This system enables real-time UAV trajectory tracking and classification, facilitating swift and accurate assessment of adherence to predefined optimal trajectories. Another major contribution of our work is the integration of a UWB system for precise UAV location tracking, complemented by our advanced image processing approach that includes a deep neural network (DNN) for interpolating missing data from images, thereby significantly enhancing the model’s ability to detect abnormal maneuvers. Our experimental results demonstrate the effectiveness of the proposed framework in UAV trajectory tracking, showcasing its robust performance irrespective of raw data quality. Furthermore, we validate the framework’s performance using a lightweight learning model, emphasizing both its computational efficiency and exceptional classification accuracy.

This paper proposed an innovative approach for reversible data hiding (RDH) within images in such a way that the image can be accurately recovered after extracting the data. The work presented a challenging task due to the need to compromise between the quality of the stego-image and the capacity of the hidden data. It is used in applications that need to reconstruct the original images without any distortion, such as medical diagnosis, remote sensing images and military maps. The aim of this paper is to study improving RDH methods using interpolation-based scheme. The proposed interpolation-based RDH (IRDH) scheme improves the embedding capacity and security over state-of-the-art schemes. We investigated the interpolation techniques improving the parabolic interpolation (PI) method to scale-up the original image, then embedding within it the secret data using newly proposed embedding technique. The experimental results demonstrate that the proposed quadratic Bezier interpolation (QBI) technique reduced the computational complexity of PI with same quality of the produced image. Additionally, the proposed cubic interpolation techniques improved the image quality of PI but with more computational complexity. On the other hand, the proposed circle interpolation (CIRI) achieved less PSNR value than PI showing interesting remarks. The general steps of this scheme are generation of the interpolated image, data embedding, data extraction and image recovery showing attractive promising research contribution.

Image interpolation is used in many areas of image processing. It is seen that many techniques developed to date have been successful in both protecting edges and increasing image quality. However, these techniques generally detect edges with gradient-based linear calculations. In this study, spiking neural networks (SNNs), which are known to successfully simulate the human visual system (HVS), are used to detect edge pixels instead of the gradient. With the help of the proposed SNN-based model, the pixels marked as edges are interpolated with a 1D directional filter. For the remaining pixels, the standard bicubic interpolation technique is used. Additionally, the success of the proposed method is compared to known methods using various metrics. The experimental results show that the proposed method is more successful than the other methods. [ABSTRACT FROM AUTHOR]

Copyright of Journal of Chongqing University of Posts & Telecommunications (Natural Science Edition) is the property of Chongqing University of Posts & Telecommunications and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Due to the recent rapid development of the ICT (Information and Communications Technology) field, the industrial sector is also experiencing rapid informatization. As a result, malware targeting information leakage and financial gain are increasingly found within IIoT (the Industrial Internet of Things). Moreover, the number of malware variants is rapidly increasing. Therefore, there is a pressing need for a safe and preemptive malware detection method capable of responding to these rapid changes. The existing malware detection method relies on specific byte sequence inclusion in a binary file. However, this method faces challenges in impacting the system or detecting variant malware. In this paper, we propose a data augmentation method based on an adversarial generative neural network to maintain a secure system and acquire necessary learning data. Specifically, we introduce a digital twin environment to safeguard systems and data. The proposed system creates fixed-size images from malware binaries in the virtual environment of the digital twin. Additionally, it generates new malware through an adversarial generative neural network. The image information produced in this manner is then employed for malware detection through deep learning. As a result, the detection performance, in preparation for the emergence of new malware, demonstrated high accuracy, exceeding 97%.

In this paper, using automated optical inspection equipment and a thermal imager, the position and the temperature of the heat source or measured object can effectively be grasped. The high-resolution depth camera is with the stereo vision distance measurement and the low-resolution thermal imager is with the long-wave infrared measurement. Based on Planck's black body radiation law and Stefan–Boltzmann law, the binocular stereo calibration of the two cameras was calculated. In order to improve the measured temperature error at different distances, equipped with Intel Real Sense Depth Camera D435, a compensator is proposed to ensure that the measured temperature of the heat source is correct and accurate. From the results, it can be clearly seen that the actual measured temperature at each distance is proportional to the temperature of the thermal image vanadium oxide, while the actual measured temperature is inversely proportional to the distance of the test object. By the proposed compensation function, the compensation temperature at varying vanadium oxide temperatures can be obtained. The errors between the average temperature at each distance and the constant temperature of the test object at 39 °C are all less than 0.1%. [ABSTRACT FROM AUTHOR]

Assessing the similarity of two images is a complex task that attracts significant efforts in the image processing community. The widely used structural similarity index measure (SSIM) addresses this problem by quantifying a perceptual structural similarity. In this paper we consider a recently introduced continuous SSIM (cSSIM), which allows one to analyze sequences of images of increasingly fine resolutions, and further extend the definition of the index to encompass the locally weighted version that is used in practice. For both the local and the global versions, we prove that the continuous index includes the classical SSIM as a special case, and we provide a precise connection between image similarity measured by the cSSIM and by the L2 norm. Using this connection, we derive bounds on the cSSIM by means of bounds on the L2 error, and we even prove that the two error measures are equivalent in certain circumstances. We exploit these results to obtain precise rates of convergence with respect to the cSSIM for several concrete image interpolation methods, and we further validate these findings by different numerical experiments. This newly established connection paves the way to obtain novel insights into the features and limitations of the SSIM, including on the effect of the local weighted window on the index performances. [ABSTRACT FROM AUTHOR]

Abstract Image interpolation has a wide range of applications such as frame rate-up conversion and free viewpoint TV. Despite significant progresses, it remains an open challenge especially for image pairs with large displacements. In this paper, we first propose a novel optimization algorithm for motion estimation, which combines the advantages of both global optimization and a local parametric transformation model. We perform optimization over dynamic label sets, which are modified after each iteration using the prior of piecewise consistency to avoid local minima. Then we apply it to an image interpolation framework including occlusion handling and intermediate image interpolation. We validate the performance of our algorithm experimentally, and show that our approach achieves state-of-the-art performance.

This paper proposes an image interpolation method with regional gradient estimation (GEI) to solve the problem of the nonlinear interpolation method not sufficiently considering non-edge pixels. First, the approach presented in this paper expanded on the edge diffusion idea used in CGI and proposed a regional gradient estimation strategy to improve the problem of gradient calculation in the CGI method. Next, the gradient value was used to determine whether a pixel was an edge pixel. Then, a 1D directional filter was employed to process edge pixels while interpolating non-edge pixels using a 2D directionless filter. Finally, we experimented with various representative interpolation methods for grayscale and color images, including the one presented in this paper, and compared them in terms of subjective results, objective criteria, and computational complexity. The experimental results showed that GEI performed better than the other methods in an experiment concerning the visual effect, objective criteria, and computational complexity. [ABSTRACT FROM AUTHOR]

In this paper we propose a new statement of the spatial increasing resolution problem of MODIS-like multi-spectral images via their fusion with Lansat-like imagery at higher resolution. We give a precise definition of the solution to the indicated problem, postulate assumptions that we impose at the initial data, establish existence and uniqueness result, and derive the corresponding necessary optimality conditions. For illustration, we supply the proposed approach by results of numerical simulations with real-life satellite images.

Pansharpening is an efficient way of producing images of higher spectral and spatial fidelity. Since pansharpening aims to generate a spectrally enhanced image with the same spatial detail content of the source panchromatic (PAN) image, the source multispectral (MS) image is upsampled to the size of the source PAN image prior to pansharpening. Several image interpolation algorithms have been proposed for this purpose, which may lead the analysts to a confusion as to which of these algorithms should be used for the best pansharpening performance. Hence, this study aimed to investigate the role of widely used image interpolation algorithms in the quality of the pansharpened images. For this purpose, the nearest neighbor interpolation, bilinear interpolation, bicubic interpolation, interpolation with a polynomial kernel of 23 coefficients (INTERP23) and cubic spline interpolation algorithms were tested through several pansharpening techniques on three test sites with different characteristics. Investigations revealed that upsampling the source MS images with the INTERP23 algorithm resulted in the pansharpened images with the optimum spectral and spatial quality. [ABSTRACT FROM AUTHOR]

Existing reversible data hiding algorithm based on interpolation technology has the advantages of large embedding capacity, but the visual quality of the watermarked image of existing algorithms is not very good. To solve this problem, a reversible data hiding algorithm based on interpolation technology and multi-layer folding is proposed. The proposed algorithm performs 2×2 non-overlapping blocks on the input image, and then uses the proposed image interpolation algorithm to generate 3×3 blocks. In order to reduce the distortion of the embedded data to the interpolated pixels, the secret data to be embedded is firstly multi-folded and encoded, and then embedded in the interpolated pixels. Experimental results show that the proposed algorithm will not have pixel overflow. Compared with similar algorithms, under the same embedding capacity, the proposed algorithm has better visual quality. In addition, the proposed algorithm is resistant to histogram and RS steganalysis. [ABSTRACT FROM AUTHOR]

This paper proposed an innovative approach for reversible data hiding (RDH) within images in such a way that the image can be accurately recovered after extracting the data. The work presented a challenging task due to the need to compromise between the quality of the stego-image and the capacity of the hidden data. It is used in applications that need to reconstruct the original images without any distortion, such as medical diagnosis, remote sensing images and military maps. The aim of this paper is to study improving RDH methods using interpolation-based scheme. The proposed interpolation-based RDH (IRDH) scheme improves the embedding capacity and security over state-of-the-art schemes. We investigated the interpolation techniques improving the parabolic interpolation (PI) method to scale-up the original image, then embedding within it the secret data using newly proposed embedding technique. The experimental results demonstrate that the proposed quadratic Bezier interpolation (QBI) technique reduced the computational complexity of PI with same quality of the produced image. Additionally, the proposed cubic interpolation techniques improved the image quality of PI but with more computational complexity. On the other hand, the proposed circle interpolation (CIRI) achieved less PSNR value than PI showing interesting remarks. The general steps of this scheme are generation of the interpolated image, data embedding, data extraction and image recovery showing attractive promising research contribution. [ABSTRACT FROM AUTHOR]

Image interpolation has a wide range of applications such as frame rate-up conversion and free viewpoint TV. Despite significant progresses, it remains an open challenge especially for image pairs with large displacements. In this paper, we first propose a novel optimization algorithm for motion estimation, which combines the advantages of both global optimization and a local parametric transformation model. We perform optimization over dynamic label sets, which are modified after each iteration using the prior of piecewise consistency to avoid local minima. Then we apply it to an image interpolation framework including occlusion handling and intermediate image interpolation. We validate the performance of our algorithm experimentally, and show that our approach achieves state-of-the-art performance. [ABSTRACT FROM AUTHOR]

Background: The issue of medial image resolution enhancement is one of the most important topics for medical imaging that helps improve the performance of many post-processing aspects like classification and segmentation towards medical diagnosis. Objective: Our aim in this paper is to evaluate different types of pixel selection models in terms of pixel originality in medical image reconstruction problems. A previous investigation showed that selecting far original pixels has highly better performance than using near unoriginal/estimated pixels while magnifying some benchmarks in digital image processing. Material and Methods: In our technical study, we apply two classical interpolators, cubic convolution (CC) and bi-linear (BL), in order to reconstruct medical images in spatial domain. In addition to the interpolators, we use some geometrical image transforms for creating the reconstruction models. Results: The results clearly demonstrate that despite the absolute preference of the original pixel selection model in the first research, we cannot see this preference in medical dataset in which the results of BL interpolator for both tested models (original and estimated pixel selection models) are approximately the same as each other and for CC interpolator, we only see a relatively better preference for the original pixel selection model. Conclusion: The current research reveals the fact that selection models are not a general factor in reconstruction problems, and the structure of the basic interpolators is also a main factor which affects the final results. In other words, some interpolators in medical dataset can be affected by the selection models, while, some cannot.