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Specular Reflections often exist in the endoscopic image, which not only hurts many computer vision algorithms but also seriously interferes with the observation and judgment of the surgeon. The information behind the recovery specular reflection areas is a necessary pre-processing step in medical image analysis and application. The existing highlight detection method is usually only suitable for medium-brightness images. The existing highlight removal method is only applicable to images without large specular regions, when dealing with high-resolution medical images with complex texture information, not only does it have a poor recovery effect, but the algorithm operation efficiency is also low. To overcome these limitations, this paper proposes a specular reflection detection and removal method for endoscopic images based on brightness classification. It can effectively detect the specular regions in endoscopic images of different brightness and can improve the operating efficiency of the algorithm while restoring the texture structure information of the high-resolution image. In addition to achieving image brightness classification and enhancing the brightness component of low-brightness images, this method also includes two new steps: In the highlight detection phase, the adaptive threshold function that changes with the brightness of the image is used to detect absolute highlights. During the highlight recovery phase, the priority function of the exemplar-based image inpainting algorithm was modified to ensure reasonable and correct repairs. At the same time, local priority computing and adaptive local search strategies were used to improve algorithm efficiency and reduce error matching. The experimental results show that compared with the other state-of-the-art, our method shows better performance in terms of qualitative and quantitative evaluations, and the algorithm efficiency is greatly improved when processing high-resolution endoscopy images.

The polynomial-based image secret sharing (ISS) scheme encodes a secret image into n shadows assigned to n participants. The secret image with high resolution is decoded by Lagrange interpolation when collecting any k or more shadows. Thus, ISS is used in applications such as distributive storage in the cloud, digital watermarking, block chain, and access control. Meaningful shadows are significant in ISS because meaningful shadows decrease the suspicion of image encryption and increase the efficiency of shadow management. Generally, previously meaningful ISS schemes were achieved through embedding the shadows into cover images using information hiding techniques and suffer from large pixel expansion and complex decoding procedure. Digital image processing, such as inpainting (texture synthesis), is a standard technique in multimedia applications. It will be highly significant if ISS can be performed in the processing of a normal digital image processing technique. Generally, the encoding method of an ISS scheme entails the use of a mathematical function that is sensitive to any slight change in the ISS output; therefore, the development of a method for performing the ISS procedure and simultaneously achieving image processing behavior is a key challenge. In this paper, we exploit the behavior ISS (BISS) and realize an image inpainting-based BISS scheme for the (k, n) threshold. Using screening operations, a secret image is encoded into the pixels of cover images by polynomial-based ISS in the processing of inpainting shadows to obtain meaningful shadows similar to the input cover images. In addition, the secret image can be losslessly decoded by Lagrange interpolation when collecting any k or more shadows. Experiments are given to confirm the efficiency of the scheme.

The elastic bending energy model is commonly used to describe the shape transformation of biological lipid vesicles, making it a classical phase field model. In this paper, by coupling the elastic bending energy with the total variation (TV) regularization, we develop an elastic bending-TV model for image inpainting. By solving the energy minimization problem of this model, we obtain the results for image processing. We adopt an operator splitting method for the model and the numerical scheme involves the introduction of two vector- and scalar-valued functions to reconstruct this functional. The energy minimization problem is transformed into finding the steady state solution of artificial time-dependent PDE systems. At each fractional step, we can find either a closed-form solution or being solved by an efficient algorithm, which is a very robust and stable algorithm. Experimental results validate the superiority of our model and the effectiveness of the scheme for image inpainting. [ABSTRACT FROM AUTHOR]

This study presents a new image inpainting model based on U-Net and incorporating the Wasserstein Generative Adversarial Network (WGAN). The model uses skip connections to connect every encoder block to the corresponding decoder block, resulting in a strictly symmetrical architecture referred to as Symmetric Connected U-Net (SC-Unet). By combining SC-Unet with a GAN, the study aims to reconstruct images more effectively and seamlessly. The traditional discriminators only differentiate the entire image as true or false. In this study, the discriminator calculated the probability of each pixel belonging to the hole and non-hole regions, which provided the generator with more gradient loss information for image inpainting. Additionally, every block of SC-Unet incorporated a Dilated Convolutional Neural Network (DCNN) to increase the receptive field of the convolutional layers. Our model also integrated Multi-Head Self-Attention (MHSA) into selected blocks to enable it to efficiently search the entire image for suitable content to fill the missing areas. This study adopts the publicly available datasets CelebA-HQ and ImageNet for evaluation. Our proposed algorithm demonstrates a 10% improvement in PSNR and a 2.94% improvement in SSIM compared to existing representative image inpainting methods in the experiment. [ABSTRACT FROM AUTHOR]

Copyright of Journal of Chongqing University of Technology (Natural Science) is the property of Chongqing University of Technology 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.)

Copyright of Journal of Computer Engineering & Applications is the property of Beijing Journal of Computer Engineering & Applications Journal Co Ltd. 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.)

Copyright of Journal of South China University of Technology (Natural Science Edition) is the property of South China University of Technology 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.)

Copyright of Chinese Journal of Liquid Crystal & Displays is the property of Chinese Journal of Liquid Crystal & Displays 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.)

In this paper, we propose two novel inertial forward–backward splitting methods for solving the constrained convex minimization of the sum of two convex functions, φ 1 + φ 2 , in Hilbert spaces and analyze their convergence behavior under some conditions. For the first method (iFBS), we use the forward–backward operator. The step size of this method depends on a constant of the Lipschitz continuity of ∇ φ 1 , hence a weak convergence result of the proposed method is established under some conditions based on a fixed point method. With the second method (iFBS-L), we modify the step size of the first method, which is independent of the Lipschitz constant of ∇ φ 1 by using a line search technique introduced by Cruz and Nghia. As an application of these methods, we compare the efficiency of the proposed methods with the inertial three-operator splitting (iTOS) method by using them to solve the constrained image inpainting problem with nuclear norm regularization. Moreover, we apply our methods to solve image restoration problems by using the least absolute shrinkage and selection operator (LASSO) model, and the results are compared with those of the forward–backward splitting method with line search (FBS-L) and the fast iterative shrinkage-thresholding method (FISTA). [ABSTRACT FROM AUTHOR]

In order to implement reasonable structure inpainting and fine texture reconstruction for large irregular missing areas with complex background, a two-stage network inpainting algorithm based on U-net edge generation and hypergraphs convolution is proposed. Firstly, the image to be repaired is fed into a coarse inpainting network based on U-net gated convolution where the context information of the image is propagated to a deeper layer through jump connection to obtain rich image detail information. Down-sampling is applied to extract the edge features of the missing area, and up-sampling is performed to restore the edge details of the missing area while hybrid dilated convolution is adopted to increase the information receptive field and further obtain image detailed texture information. Subsequently, the coarse inpainting results are inputted into the refine inpainting network with hypergraphs convolution to capture and learn the hypergraphs structure in the input image, and the cross-correlation matrix of spatial features is implemented to capture the spatial feature structure as well as to further improve the structural integrity and fine-grained details. Finally, the refined inpainting results are input into the discriminator for discrimination optimization to further improve the inpainting results. The experimental simulation is carried out on the internationally published dataset. The experimental results demonstrate that the proposed algorithm can generate a reasonable structure with good color consistency and abundant detail texture under the condition of large-area loss, and the visual effect, PSNR, SSIM and L1 loss are superior over those of the compared algorithms. [ABSTRACT FROM AUTHOR]

In recent years, the use of deep learning in image inpainting has yielded positive results. However, existing image inpainting algorithms do not pay sufficient attention to the structural and textural features of the image when inpainting, which leads to issues in the inpainting results such as blurring and distortion. To solve the above problems, a channel attention mechanism was introduced to emphasize the importance of structure and texture after extraction by the convolutional network. A bidirectional gated feature fusion module was employed to exchange and fuse the structural and textural features, ensuring the overall consistency of the image. In addition, the features of the image were better captured by selecting a deformable convolution that can adapt the receptive field to replace the ordinary convolution in the contextual feature aggregation module. This resulted in highly vivid and realistic restoration results with more reasonable details. The experiments showed that, compared with the current mainstream network, the repair results of this algorithm were more realistic, and the superiority of this algorithm was proved by qualitative and quantitative experiments. [ABSTRACT FROM AUTHOR]

For a damaged image, recovering an image with missing entire rows or columns is a challenging problem arising in many real applications, such as digital image inpainting. For this kind of information missing situation, the diffusion-based inpainting methods are tend to produce blur, the exemplar-based methods are prone to error filling and the neural network-based methods are highly dependent on data. Many existing approaches formulate this problem as a general low-rank matrix approximate one which cannot handle this special structural missing very well. In this paper, we propose a novel image inpainting algorithm named nonlocal low-rank tensor completion (NLLRTC) based on the nonlocal self-similarity prior and the low-rank prior. By using the nonlocal self-similarity of image patches, we directly stack these patches into a three-dimensional similar tensor instead of pulling them into column vectors, then the similar tensor can be completed by tensor ring (TR) decomposition. By leveraging the alternating direction method under the augmented Lagrangian multiplier framework, the optimization results can be obtained. Moreover, a weighted nuclear norm is added to the tensor completion model to achieve better inpainting performance, which we call weighted nonlocal low-rank tensor completion (WNLLRTC) algorithm. Our empirical studies show encouraging results on both quantitative assessment and visual interpretation of our proposed methods in comparison to some state-of-the-art algorithms. [ABSTRACT FROM AUTHOR]

Copyright of Journal of Frontiers of Computer Science & Technology is the property of Beijing Journal of Computer Engineering & Applications Journal Co Ltd. 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.)

In general, CNN-based inpainting can recover local patterns effectively using convolutional filters, but it may not exploit global correlation fully. On the other hand, transformer-based inpainting can fill in large holes faithfully based on global correlation, rather than local one. In this paper, we propose a novel image inpainting algorithm, called local and global mixture (LGM), to take advantage of the strengths of both approaches and compensate for their weaknesses. The LGM network comprises the local inpainting network (LIN) and the global inpainting network (GIN) in parallel, which are based on convolutional layers and transformer blocks, respectively, and exchange their intermediate results with each other. Furthermore, we develop an error propagation model with a continuous error mask, updated in LIN but used in both LIN and GIN to provide more reliable inpainting results. Extensive experiments demonstrate that the proposed LGM algorithm provides excellent inpainting performance, which indicates the efficacy of the parallel combination of LIN and GIN and the effectiveness of the error propagation model. [ABSTRACT FROM AUTHOR]

This paper addresses the problem of synthesizing person silhouette images. Previous methods on human‐centric image synthesis deal with normal images taken under consistent lighting conditions. However, a silhouette image has an inconsistent lighting appearance, in which the dark subject and the bright background form a strong contrast. Therefore, it brings great challenges to person silhouette image synthesis. We present a staged method to synthesize realistic person silhouette photos with various poses. The purpose of our method is to give ordinary users the opportunities to interactively adjust the pose of a person in a silhouette photo. The method consists of four main steps: person detection, person analysis, interactive deformation, and image synthesis. To obtain the shape of the target person, we propose a silhouette image segmentation algorithm combined with person detection. Moreover, we also present an effective image inpainting approach to complement a silhouette image with an irregular hole. Experimental results show that the proposed method can generate a set of realistic person silhouette images with interactively changed poses. [ABSTRACT FROM AUTHOR]

A mural is any piece of artwork sculpted or applied directly on a wall, ceiling or other permanent surface. This artwork symbolizes various culture's, traditions, historical events, spiritual stories, and civilizations of respective societies of ancient times. But these mural paintings are subjected to degradation either by various natural causes as well as pollution or by human beings without knowing their value. Restoring these paintings requires skilled artisans who are hard to find these days. Consequently, an efficient image restoration technique is required to meet the particular needs of the paintings. Existing in-painting algorithms largely use pixel-based textural reconstruction. The technique, however, does not work well for images with large, degraded portions. and also fails in the restoration of the structure. To resolve these drawbacks, we propose a combined technique for the textural and structural reconstruction of ancient murals. The proposed Extended Exemplar-based Region-Filling Algorithm uses a patch-based reconstruction procedure and masked images are created automatically using the Dynamic Mask Generation Algorithm. The deteriorated portions are identified by creating masks, and masks are created in such a way that degraded portions have a pixel intensity value of one and the remaining part has a value of zero, and filling is done by analyzing the surrounding pixel values of the degraded pixel. The algorithm reconstructs the structure of the paintings efficiently by generating sketches. The proposed technique reconstructs both the structure and textural information, and ensures efficient reconstructed results, compared to existing in-painting techniques. Performance is evaluated by metrics such as the Mean Square Error (MSE), Peak Signal-to-Noise Ratio (PSNR), and Structural Similarity Index (SSIM). [ABSTRACT FROM AUTHOR]

In recent trends, the image compression plays a pivotal part in conveying information throughout the world. It helps to reduce the redundant data of the image for the purpose of storing as well transmission in a cost effective manner. There are several conventional techniques that are used for image compression, but still it lacks in some issues such as increased error, inaccurate results, ineffective, etc. Thus, a novel hybrid image compression approach is proposed in this work for better image compression. Initially the input image is decomposed as structural and textural regions of the image. From the decomposed image, the process of feature extraction is carried out. Here the features from structural regions are extracted using Partial Differential Equation (PDE) based inpainting approach. The features from the textural features are extracted using the texture based algorithm. Among these extracted features, the redundant details are dropped to preserve the significant details. From this, the compressed image is obtained at the receiver side, the significant details from the compressed image performs region completion operation. Here the holes in structural regions are filled by PDE based interpolation technique and the textural regions are filled using texture algorithm, also the color information is synthesized along with this textural detail. Finally, the inpainting algorithm is used for correcting the discontinuities. The experimental results prove the superiority of this hybrid image compression. [ABSTRACT FROM AUTHOR]

Tensor recovery has recently arisen in a lot of application fields, such as transportation, medical imaging, and remote sensing. Under the assumption that signals possess sparse and/or low-rank structures, many tensor recovery methods have been developed to apply various regularization techniques together with the operator-splitting type of algorithms. Due to the unprecedented growth of data, it becomes increasingly desirable to use streamlined algorithms to achieve real-time computation, such as stochastic optimization algorithms that have recently emerged as an efficient family of methods in machine learning. In this work, we propose a novel algorithmic framework based on the Kaczmarz algorithm for tensor recovery. We provide thorough convergence analysis and its applications from the vector case to the tensor one. Numerical results on a variety of tensor recovery applications, including sparse signal recovery, low-rank tensor recovery, image inpainting, and deconvolution, illustrate the enormous potential of the proposed methods. [ABSTRACT FROM AUTHOR]

In the development of imaging science and image processing request in our daily life, inpainting large regions always plays an important role. However, the existing local regularized models and some patch manifold based non-local models are often not effective in restoring the features and patterns in the large missing regions. In this paper, we will apply a strategy of inpainting from outside to inside and propose a re-weighted matching algorithm by closest patch (RWCP), contributing to further enhancing the features in the missing large regions. Additionally, we propose another re-weighted matching algorithm by distance-based weighted average (RWWA), leading to a result with higher PSNR value in some cases. Numerical simulations will demonstrate that for large region inpainting, the proposed method is more applicable than most canonical methods. Moreover, combined with image denoising methods, the proposed model is also applicable for noisy image restoration with large missing regions. [ABSTRACT FROM AUTHOR]

Image inpainting is a process of reconstructing an incomplete image from the available information in a visually plausible way. In the proposed framework, existing image inpainting methods are classified in a new perspective. The information which is referred to, while reconstructing an image, is a critical factor of inpainting algorithms. Source of this information can be host image itself or an external source. The proposed framework broadly classifies inpainting algorithms into introspective and extrospective categories based on the source of information. Various parameters influencing the algorithms under these categories are identified in the proposed framework. A comprehensive list of all publicly available datasets along with the references are also summarized. Additionally, an in-depth analysis of the results obtained with the surveyed techniques is performed based on quantitative and qualitative parameters. The proposed framework aids the user in identifying the most suitable algorithm for various inpainting scenarios. [ABSTRACT FROM AUTHOR]

Objective evaluation of images is one of the most essential and practical aspects of image inpainting. The existing objective evaluation methods of image inpainting are functional only on an individual basis and do not provide an accurate and useful objective evaluation of inpainted images. Currently, there is no objective measure for evaluating inpainted images. In this study, an objective evaluation method was developed for image inpainting. In the proposed method, first, 100 images were inpainted using an exemplar-based algorithm. Then, the saliency map and its complementary region in the original image were obtained and a new objective measure was proposed for the evaluation of inpainted images based on the saliency map features. To make the assessment more realistic and comparable to human judgments, two terms, namely penalty and compensation, were taken into account. To assess the performance of our proposed objective measure, the inpainted images were also evaluated using a subjective test. The experiments demonstrates that the proposed objective measure correlated with the qualitative opinion in a human observer study. Finally, the objective measure was compared against three other measures, and the results showed that our proposed objective measure performed better than the other evaluation measures. [ABSTRACT FROM AUTHOR]

Existing image inpainting algorithms based on neural network models are affected by structural distortions and blurred textures on visible connectivity. As a result, overfitting and overlearning phenomena can easily emerge during the image inpainting procedure. Image inpainting refers to the repairing of missing parts of an image, given an image that is broken or incomplete. After the repairing operation is complete, there are obvious signs of repair in damaged areas, semantic discontinuities, and unclearness. This paper proposes an improved image inpainting method based on a new encoder combined with a context loss function. In order to obtain clear repaired images and ensure that the semantic features of images are fully learned, a generative network based on the fusion model of squeeze-and-excitation networks deep residual learning has been proposed to improve the application of network features in order to obtain clear images and reduce network parameters. At the same time, a discriminative network based on the squeeze-and-excitation residual Network has been proposed to strengthen the capability of the discriminative network. In order to make the generated image more realistic, so that the restored image will be more similar to the original image, a joint context-awareness loss training method (contextual perception loss network) has also been proposed to generate the similarity of the local features of the network constraint, with the result that the repaired image is closer to the original picture and more realistic. The experimental results can demonstrate that the proposed algorithm demonstrates better adaptive capability than the comparison algorithms on a number of image categories. In addition, the processing results of the image inpainting procedure were also superior to those of five state-of-the-art algorithms. [ABSTRACT FROM AUTHOR]

All existing image inpainting methods based on neural network models are affected by structural distortions and blurred textures on visible connectivity, such that overfitting and overlearning phenomena can easily emerge in the image inpainting processing procedure. Accordingly, in an attempt to address the defects of image inpainting algorithm, such as long iteration time, poor adaptability and unsatisfactory repairing effects, the image inpainting algorithm of improved Generative Adversarial Networks based on deep learning method of Two-Discriminations Network has been proposed in the paper. The proposed method uses image inpainting network, global discrimination network and local discrimination network to create a fusion network to apply computational images. In the training procedure of proposed algorithm, the network of image inpainting algorithm uses similar patching method to fill the broken area in image and set it as input training objects, which greatly improves the speed and quality of image inpainting. The global discrimination network uses global structure with marginal information and feature information to judge the completed image, meaning that it comprehensively achieves visible connectivity. As local discrimination network can judge the computational images, it has also been trained with assisted feature patches found on multiple images. Furthermore, the proposed method can enhance the discriminant capability and solve the problem that the image inpainting network has easily been overfitting when the features are too concentrated and limited in number to process. Our results of designed experiments demonstrate that proposed algorithm has better adaptive capability on several image categories than those state-of-the-arts. [ABSTRACT FROM AUTHOR]

In this paper, we present a new algorithm for image inpainting using low dimensional feature space. In our method, projecting a low dimensional space from the original space is accomplished firstly using SVD, which is named low rank component, and then the missing pixels are filled in the new space. Finally, the original image is inpainted so that adaptive patch size is considered by quad-tree based on the previous step. In our algorithm, the missing pixels in the target region are estimated twice, one in low dimension feature space and another in the original space. It is noticeable that both processes estimate the unknown pixels using patch-based idea and rank lowering concept. Experimental results of this algorithm show better consistency in comparison with state-of-the-art methods. [ABSTRACT FROM AUTHOR]

Inpainting has been continuously studied in the field of computer vision. As artificial intelligence technology developed, deep learning technology was introduced in inpainting research, helping to improve performance. Currently, the input target of an inpainting algorithm using deep learning has been studied from a single image to a video. However, deep learning-based inpainting technology for panoramic images has not been actively studied. We propose a 360-degree panoramic image inpainting method using generative adversarial networks (GANs). The proposed network inputs a 360-degree equirectangular format panoramic image converts it into a cube map format, which has relatively little distortion and uses it as a training network. Since the cube map format is used, the correlation of the six sides of the cube map should be considered. Therefore, all faces of the cube map are used as input for the whole discriminative network, and each face of the cube map is used as input for the slice discriminative network to determine the authenticity of the generated image. The proposed network performed qualitatively better than existing single-image inpainting algorithms and baseline algorithms. [ABSTRACT FROM AUTHOR]

Images created from measurements made by wireline microresistivity imaging tools have longitudinal gaps when the well circumference exceeds the total width of the pad-mounted electrode arrays. The gap size depends on the tool design and borehole size, and the null data in these gaps negatively affect the quantitative evaluation of reservoirs. Images with linear and texture features obtained from microresistivity image logs have distinct dual fabric features because of logging principles and various geological phenomena. Linear image features usually include phenomena such as fractures, bedding, and unconformities. Contrarily, texture-based image features usually indicate phenomena such as vugs and rock matrices. According to the characteristics of this fabric-based binary image structure and guided by the practice of geological interpretation, an adaptive inpainting method for the blank gaps in microresistivity image logs is proposed. For images with linear features, a sinusoidal tracking inpainting algorithm based on an evaluation of the validity and continuity of pixel sets is used. Contrarily, the most similar target transplantation algorithm is applied to texture-based images. The results obtained for measured electrical imaging data showed that the full borehole image obtained by the proposed method, whether it was a linear structural image reflecting fracture and bedding or texture-based image reflecting the matrix and pore of rock, had substantially good inpainting quality with enhanced visual connectivity. The proposed method was effective for inpainting electrical image logs with large gaps and high angle fractures with high heterogeneity. Moreover, ladder and block artifacts were rare, and the inpainting marks were not obvious. In addition, detailed full borehole images obtained by the proposed method will provide an essential basis for interpreting geological phenomena and reservoir parameters. [ABSTRACT FROM AUTHOR]

Link prediction is a fundamental task that predicts whether a link exists between two nodes based on the currently observed network. Existing approaches such as heuristic-based algorithms assume that two nodes are likely to have a link in a network. In fact, they limit algorithm effectiveness when the assumptions are not correct. Moreover, these link prediction algorithms lack generalization ability, which indicates that they have different effects on different types of networks. For example, the common neighbours algorithm works well on social networks, but it shows poor performance on electric power networks. Inspired by the image inpainting technology of generative adversarial networks and the adjacency matrix representation of networks, we propose a new framework for link prediction based on the image inpainting method, named the Generative Image Inpainting for Link Prediction algorithm (GIILP), to address these problems. The key idea of the GIILP is that the network can be converted into an image (the image is a form of the adjacency matrix (two dimensions)). Pixel values represent the likelihood of two nodes. Thus, the problem of predicting a possible link between nodes is converted into filling missing pixels in an image. The link information of the network can be expressed by the image information, which means that our algorithm does not need assumptions such as heuristics, and works regardless of the dataset type. The experimental results on multiple link prediction public datasets demonstrate that our algorithm has an advantage over other algorithms, including heuristic-based and deep learning methods. [ABSTRACT FROM AUTHOR]

Various problems existed in the image inpainting algorithms, which can't meet people's requirements visually. Aiming at the defects of the existing image inpainting algorithms, such as low accuracy, poor visual consistency, and unstable training, an improved image inpainting algorithm used on a multi-scale generative adversarial network (GAN) and neighbourhood model have been proposed in the paper. The proposed algorithm mainly improves the network structure of the discriminator, and introduces a multi-scale discriminator based on the global discriminator and the local discriminator. The multi-scale discriminators were trained on images of different resolutions. Discriminators of different scales have different receptive fields, which can guide the generator to generate more global image views and finer details. Aiming at the problem of gradient disappearance or gradient explosion that often occurs in GAN training, the method of WGAN (Wasserstein GAN) has been used to simulate the sample data distribution using EM distance. The proposed model has been trained and tested on the CelebA, ImageNet, and Place2. The experimental results show that compared with the previous algorithm model, the proposed algorithm improves the accuracy of image inpainting and can generate more realistic repairing images, and it is suitable for many types of images. [ABSTRACT FROM AUTHOR]

In this paper, we present a new algorithm for image inpainting using structure and texture information. Our image decomposition to texture and structure is accomplished by the SVD method in the primary step, and then an algorithm for texture inpainting is applied. At the next level, edge detection is used in target region related to inpainted texture component. The detected edges demonstrate border of different textures in the target region, and the boundary pixels are ignored from mask temporarily. The other target pixels should be primarily inpainted, and then border pixels would be filled subsequently. Experimental results of this algorithm show better consistency in comparison with state of the art methods. [ABSTRACT FROM AUTHOR]

Metal implants often produce severe artifacts in the reconstructed computed tomography (CT) images, causing information and image detail loss and making the CT images diagnostically unusable. In order to eliminate the metal artifacts and enhance the diagnostic value of the reconstructed CT images, a post-processing metal artifact reduction algorithm, based on a tissue-class model segmented by thresholding and k-means clustering with spatial information, is proposed. The image inpainting technique is incorporated into the algorithm to improve the segmentation accuracy for CT images severely corrupted by metal artifacts. A study of a water phantom and of two sets of clinical CT images was performed to test the algorithm performance. The proposed method effectively eliminates typical metal artifacts, restores the average CT numbers of different tissues to the proper levels, and preserves the edge and contrast information, thus allowing the accurate reconstruction of the tissue attenuation map. The quality of the artifact-corrected CT images allows them to be subsequently used in other clinical applications, such as three-dimensional rendering, dose estimation for radiotherapy, attenuation correction for PET and SPECT, etc. The algorithm does not rely on the use of the raw sinogram and so is not limited by the proprietary format restrictions. [ABSTRACT FROM AUTHOR]

Most image inpainting algorithms have problems such as fuzzy images, texture distortion and semantic inaccuracy, and the image inpainting effect is limited when processing photos with large missing sections and resolution levels. The paper proposes an effective image inpainting algorithm via partial multi-scale channel attention mechanism and deep neural networks to address the above phenomenon that existing image inpainting methods using deep learning modules have insufficient perception and representation capabilities for multi-scale features with high proportion of irregular defects. Initially, we used the Res-U-Net module as a generator. The U-Net-like backbone network topology can achieve the encoding and decoding stages of damaged images. Secondly, the residual network structure was built in the encoder and decoder to improve the ability of the proposed network to extract and display the features of the damaged images. Finally, the partial multi-scale channel attention module was inserted in the skip connection with the decoder to increase the efficiency of using the low-level features of the original images. The experimental results of the research can show that the proposed method outperforms state-of-the-art methods in terms of subjective visual perception and objective evaluation indicators on the CelebA , Places2 and Paris Street View datasets. • The model has improved the ability to extract and express multi-scale features. • The method had included partial multi-scale channel attention and residual network. • The backbone network structure can realize encoding and decoding stage. • The structure of residual networks had constructed to enhance the abilities. [ABSTRACT FROM AUTHOR]

In order to solve the problems of the criminisi algorithm in inpainting thangka image, such as the mistake matching phenomenon, image structure information inconsistent and the inaccurate matching standards, the new image inpainting algorithm based on thangka image structure information is proposed in this paper. The following three steps are the keys of this method. (1) The correlation of repaired block and its neighborhood block is introduced and the priority of formula is improved; (2) The exemplar-based size selection is improved, the adaptive patch size is automatically adjusted according to the exemplar-based information changes; (3) In order to solve mistake matching problem, the structure information of thangka image and color of Euclidean distance are combined as the new matching criterion. The experimental results show that the mistake matching phenomenon by the proposed method for thangka image is significantly reduced, the structure of thangka image is more fluent and smooth than them in comparative literature. [ABSTRACT FROM AUTHOR]

In order to retrieve large scale damaged image with rich geometry structure and texture information, the novel image inpainting algorithm based on the neighbourhood reference priority can not only maintain image character but also improve image inpainting speed has been proposed in the paper. The problems of an image inpainting process can be translated into the best sample searching process. First, the extracting structure information of image and the dividing sample region into sub-regions server. Second, in order to adjust the neglect of structure of matching method named SSD, introducing structure symmetry matching constraint into matching method, it avoids matching mistakenly and improves sample matching precision and searching efficiency. Then, improving priority equations by bringing in structure weight and confidence, highlighted the effect of structure to inpainting sequence. Finally, computing priority of four-domains neighbour by computation overlapping information between object patch and sample patch, so that referring to secure information of four-domains neighbour, to prune sample dataset and search optimal sample. The experimental results have demonstrated that the proposed algorithm can overcome problems like texture blurring and structure dislocations and so on, the PSNR of the improved algorithm has been increased by 0.5–1 dB comparing other contrast methods while speeding up the image inpainting process, recovered image is much continuous for visuality. Meanwhile, it can recover efficiently common damage images and be more pervasive. [ABSTRACT FROM AUTHOR]

Highlights • We present a new boosting technique for TGV based image denoising and inpainting. • The proposed technique first takes average of restored image with observed image. • It then operates the TGV algorithm with the average as input with dynamic parameter. • Algorithm is discussed on effect of parameters and compared with several methods. Abstract In this paper we present a new adaptive boosting technique for total generalized variation (TGV) based image denoising and inpainting. Instead of the strengthening and substracting steps in existing boosting techniques, the proposed technique is iteratively operated by two steps: the first step is to take average of restored image with observed image, and updated parameter; the second step is to operate the TGV restoration algorithm with the average and dynamic parameter. For each iteration, as the input contains more correct information, the restoration algorithm can produce signals with more details. We have solved our boosting TGV model by primal-dual method, and applied the boosting TGV technique for gray/color image denoising and inpainting. Our algorithms have been discussed about influence of parameters, computational cost and compared with several typical existing methods. Plenty of experimental results show that our method can produce images with more structures and prevent staircase artifacts effectively. [ABSTRACT FROM AUTHOR]

Highlights • A photo-realistic continuous digital zooming algorithm for an asymmetric dual-camera system was proposed. • Discussed drawbacks of related technology, and proposed improvements to overcome them. • A super-resolution framework was designed to support continuous digital zooming. • A band-inpainting algorithm was designed to reproduce texture regions in wide-view images. • Qualitative and quantitative comparisons with state-of-the-art algorithms were provided. Abstract Asymmetrical dual camera systems are becoming more and more popular among many commercial products and they are expected to provide digital zooming images. Existing related algorithms such as super-solution and image fusion fail to reconstruct photo-realistic results because of the sampling rate inconsistency, especially when the focal lengths of cameras vary largely. Nevertheless, magnification factor of super-resolution algorithms are fixed and inflexible as well. By looking into the inherent drawbacks of existing super-resolution algorithm, a new photo-realistic continuous digital zooming algorithm is then proposed in this paper on the basis of a novel super-resolution framework and a band-inpainting algorithm. Experiment result illustrates that proposed algorithm combines the advantages of both super-resolution algorithm and image inpainting algorithm: it reconstructs band information with super-resolution methods, and introduce image inpainting algorithm to reconstruct poorly super-resolved texture areas to produce state-of-the-art photo-realistic digital zooming images. [ABSTRACT FROM AUTHOR]

Highlights • A MRF-based inpainting algorithm for maintaining structure coherence is proposed. • Selecting proper offsets for energy function determines the inpainting results. • Appropriate direction structure feature is helpful to improve the inpainting performance and efficiency. Abstract Image inpainting provides a means for restoration of an image with some damaged portions and has been widely applied to many fields. However, many existing inpainting methods still suffer the difficulties of effectively and efficiently maintaining structure coherence. To tackle the problems, this paper proposes a direction structure distribution analysis strategy for Markov Random Field (MRF) -based image inpainting algorithms. In the strategy, the desired direction structures (DDS) are selected and applied to guide the inpainting process. Given a degraded image, four kinds of direction edge information are extracted by super-wavelet transform, canny and dilation operators. Then local direction edge gradient magnitude is applied for direction structure distribution analysis and DDS are selected according to gradient variance. Afterwards, offsets of similar patches are calculated and only a few dominated offsets are chosen as candidate offsets. Finally, graph-cut optimization technology is utilized to solve energy function. Experimental results show that the proposed method achieves generally better performance than nine state-of-the-art methods in terms of the abilities of maintaining structure coherence and the computational cost on inpainting different kinds of degraded images. [ABSTRACT FROM AUTHOR]

Highlights • A framework of uman-in-the-loop style image completion is proposed. • Failure inpainted regions are automatically detected using a deep neural network. • A set of features tailored for patch-based image completion are used. • Our system is comparable to human-in-the-loop completion by real human annotators. Image completion is a technique to fill missing regions in a damaged or redacted image. A patch-based approach is one of major approaches, which solves an optimization problem that involves pixel values in missing regions and similar image patch search. One major problem of this approach is that it sometimes duplicates implausible texture in the image or overly smooths down a missing region when the algorithm cannot find better patches. As a practical remedy, the user may provide an interaction to identify such regions and re-apply image completion iteratively until she/he gets a desirable result. In this work, inspired by this idea, we propose a framework of human-in-the-loop style image completion with automatic failure detection using a deep neural network instead of human interaction. Our neural network takes small patches extracted from multiple feature maps obtained from the completion process as input for the automated interaction process, which is iterated several times. We experimentally show that our neural network outperforms a conventional linear support vector machine. Our subjective evaluation demonstrates that our method drastically improves the visual quality of resulting images compared to non-iterative application. [ABSTRACT FROM AUTHOR]

A novel image completion method is proposed that uses the advantage of planar structural information to fill corrupted portions of an image. First, in estimating parameters of the projection plane, the image is divided into several planes, and their planar structural information is analyzed. Second, in calculating the a priori probability of patch and patch offset regularity, this information is converted into a constraint condition to guide the process of filling the hole. Experimental results show that the proposed algorithm is fast and effective, and ensures the structure continuity of the damaged region and smoothness of the texture. [ABSTRACT FROM AUTHOR]

Image inpainting is an effective method to repair damaged image. In this paper, we mainly studied some typical image inpainting models and analyzed these corresponding repair qualities. Firstly, we studied three typical algorithms based on PDE (Partial Differential Equation) model to repair images whose damaged areas are relative small. And then we studied the image inpainting algorithm based on texture synthesis. This kind of inpainting algorithm is suitable for image with large damaged area. Finally, we studied Criminisi inpainting algorithm based on image segmentation in detail, on which we proposed an improved priority formula image repair algorithm, the simulation results indicate that the improved algorithm of this paper showed better performance in repair quality from both subjective and objective aspects. [ABSTRACT FROM AUTHOR]

The traditional ( k , n ) threshold secret image sharing (SIS) schemes dealt with the full secret image neglecting the possible situation that only part of the secret image needs protection. However, in some applications, only target part of the secret image may need to be protected while other parts may be not in a full image. In this paper, we consider the partial secret image sharing (PSIS) issue as well as propose a PSIS scheme for ( k , n ) threshold based on image inpainting and linear congruence (LC)-based SIS. The full secret image including the secret target part and other parts will be recovered by collecting any k or more shadow images, which can be further reconstructed losslessly by adding all the inpainted meaningful shadow images. Furthermore, the proposed scheme can share irregular target in a progressive way. Experiments are conducted to evaluate the efficiency of the proposed scheme. [ABSTRACT FROM AUTHOR]

This paper presents a new algorithm for fast exemplar-based image inpainting by using image pyramid and a novel pruning scheme. The proposed technique separates the inpainting process into three major steps. First the model determines which patch belonging to the inpainting region should be filled first. Then the pruning system analyzes the selected patch and weeds out regions having no chance to be the best matching patch. Finally, it performs a fast texture synthesis based on greedy search method in the remaining regions to fill the selected patch. Compared with previous methods, the proposed algorithm is much more efficient computationally. Experiments on synthetic and natural images show the advantages of the proposed algorithm, where it is shown to compare favorably with contemporary state-of-the-art schemes. [ABSTRACT FROM AUTHOR]

This paper presents a novel and efficient algorithm for non-texture inpainting of images based on using the dominant orientation of local gradients. It first introduces the concept of a new matrix called orientation matrix and then uses it for faster and better inpainting. The process of propagating information is carried out by using a new formulation which leads to much more efficient computations than the previous methods. The gain is both in terms of computational complexity and visual quality. The promising results in contexts of text, scratch, and block loss inpainting demonstrate the effectiveness of the proposed method. [ABSTRACT FROM AUTHOR]

For curves or general interfaces, Euler's elastica energy has a wide range of applications in computer vision and image processing. It is however difficult to minimize the functionals related to the elastica energy due to its non-convexity, nonlinearity and higher order with derivatives. In this paper, we propose a very simple way to combine level set and binary representations for interfaces and then use a fast algorithm to minimize the functionals involving the elastica energy. The proposed algorithm essentially just needs to solve a total variation type minimization problem and a re-distance problem. Nowadays, there are many fast algorithms to solve these two problems and thus the overall efficiency of the proposed algorithm is very high. We then apply the new Euler's elastica minimization algorithm to image segmentation, image inpainting and illusory shape reconstruction problems. Extensive experimental results are finally conducted to validate the effectiveness of the proposed algorithm. [ABSTRACT FROM AUTHOR]

Exemplar-based image inpainting, as proposed by Criminisi et al. (IEEE Trans Image Process 13(9):1200-1212, 2004), fills missing regions by using a similar exemplar. However, when the missing region is a unique texture patch, an incorrect texture is filled in the missing region because a similar exemplar of damaged patch could not be found. A new image inpainting method based on an eight-direction or arbitrary direction symmetrical exemplar is proposed, suitable for damaged images containing local symmetry. The following three steps are the keys of this method. (1) According to certain similarity criteria, the symmetrical exemplars of damaged regions in eight directions or arbitrary directions are found. (2) The most similar symmetrical exemplar is selected from eight-direction or arbitrary-direction symmetrical exemplars. (3) Finally, the damaged region is filled using the most similar symmetrical exemplar. It is shown that the results of image inpainting are good when missing image regions have similar symmetry. Image inpainting is a single, efficient method. In addition, a new evaluation method of image restoration results based on similar exemplars is proposed for the inpainting effect, which is closely related to the repair algorithm. Therefore, the methods can more objectively measure the inpainting effect. [ABSTRACT FROM AUTHOR]

In this paper, p-Laplace variational image inpainting model with symmetric Riesz fractional differential filter is proposed. Variational inpainting models are very useful to restore many smaller damaged regions of an image. Integer order variational image inpainting models (especially second and fourth order) work well to complete the unknown regions. However, in the process of inpainting with these models, any of the unindented visual effects such as staircasing, speckle noise, edge blurring, or loss in contrast are introduced. Recently, fractional derivative operators were applied by researchers to restore the damaged regions of the image. Experimentation with these operators for variational image inpainting led to the conclusion that second order symmetric Riesz fractional differential operator not only completes the damaged regions effectively, but also reducing unintended effects. In this article, The filling process of damaged regions is based on the fractional central curvature term. The proposed model is compared with integer order variational models and also Grunwald-Letnikov fractional derivative based variational inpainting in terms of peak signal to noise ratio, structural similarity and mutual information. [ABSTRACT FROM AUTHOR]

To solving the problems that the existing image inpainting methods lack authenticity, do not deal with the information of missing and non-missing regions flexibly, and do not deal with the image feature information on different stages effectively, we propose an image restoration method combining Semantic Priors and Deep Attention Residual Group. The image restoration method mainly consists of Semantic Priors Network, Deep Attention Residual Group, and Full-scale Skip Connection. The Semantic Priors Network learns the complete semantic prior information of the visual elements in the missing regions and completes the missing regions with the learned semantic information. The deep attention residual set allows the generator to focus more on not only the missing regions of the image but also to learn the features of each channel adaptive. The full-scale jump joins can combine the low-level feature maps containing image boundaries with the high-level feature maps containing image textures and details to repair the missing regions. In this paper, a full comparison experiment is conducted on the CelebA-HQ and the Paris StreetView datasets, and the experimental results show that proposed method outperforms the current representative state-of-the-art image restoration methods. [ABSTRACT FROM AUTHOR]

We propose a constrained inpainting model to recover an image from its incomplete and/or inaccurate wavelet coefficients. The objective functional of the proposed model uses the ℓ 0 norm to promote the sparsity of the resulting image in a tight framelet system. To overcome the algorithmic difficulty caused by the use of the ℓ 0 norm, we approximate the ℓ 0 norm by its Moreau envelope. A fixed-point proximity algorithm is developed to solve the new approximation optimization model and the convergence analysis of the algorithm is provided. The proposed algorithm can be accelerated by the FISTA technique and we also develop an adaptive method to determine the approximation parameter to further speed up the algorithm. We demonstrate that the rows of the discrete cosine transform matrix can generate a redundant tight framelet system with symmetric boundary condition, which has good ability to extract information from incomplete wavelet coefficients. Using the tight framelet system, our numerical experiments show that the proposed model and the related fixed-point algorithm can recover images with much higher quality in terms of the PSNR values and visual quality of the restored images than the models based on the ℓ 1 norm and the total variation. [ABSTRACT FROM AUTHOR]

• The proposed Anti-Gravity Stem-Seeking (AGSS) root image restoration algorithm can quickly and effectively repair root images. • AGSS root image restoration algorithm has good universality and does not need training data and high-performance hardware platforms. • Drought and waterlogging resistances of maizes can be effectively evaluated by the phenotypic parameters of repaired seed roots. Root phenotype detection is key to cultivating seeds with excellent traits, and requires a complete root image. However, soil occlusion, uneven lighting, and other factors cause broken points and segments in the root image. To solve this problem, an anti-gravity stem-seeking (AGSS) root image restoration algorithm is proposed in this paper to repair root images and extract root phenotype information for different resistant maize seeds. First, the obtained root image was processed using uniform illumination, grayscale, binarization, and morphological filtering to separate it from the background. Subsequently, a root skeleton map was generated using a thinning algorithm for pixel-level image processing, and the Taproot junction G was obtained. Subsequently, root pixel coordinates were obtained by traversing the root skeleton map. All root-segment endpoint coordinates were obtained using the endpoint judgment rule and stored in the endpoint list. The lateral and primary root endpoints were separated based on the lateral root judgment rule, and stored in the side root and primary root endpoint lists, respectively. Subsequently, the primary root endpoints were processed and fitted in the order short to long using arbitrary-two-endpoint spacing until all breakpoints of the primary root were found. The coordinates of the top endpoints of each principal root were obtained. Finally, the top endpoint was connected to point G based on the Bezier curve-fitting method to achieve complete root repair. The proposed AGSS root image restoration algorithm was applied to detect the root systems of maize with different resistances and wheat to evaluate its performance against the standard dataset. The results indicated a detection accuracy of greater than 90% for root taproot length and diameter. It was also found that maize drought resistance was positively correlated with root length and diameter, but negatively with the lateral root number. In contrast, the waterlogging and salt resistance traits of maize were positively correlated with the number of lateral roots. In conclusion, the proposed AGSS root image restoration algorithm can quickly and effectively repair root images, is suitable for different resistance evaluations of maize seeds, and is conducive to the detection of root phenotypes. Compared with deep learning methods, this algorithm displays advantages of fast repair, low hardware platform requirements, and less requirement of training images. The algorithm is highly suitable for deploying in small embedded systems, with broad application prospects. [ABSTRACT FROM AUTHOR]

The augmented Lagrangian method (ALM) is a popular method for solving linearly constrained convex minimization problems, and it has been used in many applications such as compressive sensing or image processing. Recently, accelerated versions of the augmented Lagrangian method (AALM) have been developed, and they assume that the subproblem can be exactly solved. However, the subproblem of the augmented Lagrangian method in general does not have a closed-form solution. In this paper, we introduce an inexact version of an accelerated augmented Lagrangian method (I-AALM), with an implementable inexact stopping condition for the subproblem. It is also proved that the convergence rate of our method remains the same as the accelerated ALM, which is $${{\mathcal {O}}}(\frac{1}{k^2})$$ with an iteration number $$k$$ . In a similar manner, we propose an inexact accelerated alternating direction method of multiplier (I-AADMM), which is an inexact version of an accelerated ADMM. Numerical applications to compressive sensing or image inpainting are also presented to validate the effectiveness of the proposed iterative algorithms. [ABSTRACT FROM AUTHOR]