Research on image Inpainting algorithm of improved GAN based on two-discriminations networks

Chen, Yuantao; Zhang, Haopeng; Liu, Linwu; Chen, Xi; Zhang, Qian; Yang, Kai; Xia, Runlong; Xie, Jiang

doi:10.1007/s10489-020-01971-2

Cited by 54 publications

(18 citation statements)

References 32 publications

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“…Only by simulating the characteristics of human perceptual organization in the algorithm design can we obtain restoration results that conform to human visual psychological habits. In the current image restoration, the similarity, proximity, and continuity of perceptual tissues are the most widely used [34,35]. e same-directional motion law of perceptual organization is also widely used in video motion segmentation.…”

Section: Description Of Image Repair Problemmentioning

confidence: 99%

Application of Adaptive Image Restoration Algorithm Based on Sparsity of Block Structure in Environmental Art Design

2021

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Image restoration is a research hotspot in computer vision and computer graphics. It uses the effective information in the image to fill in the information of the designated damaged area. This has high application value in environmental design, film and television special effects production, old photo restoration, and removal of text or obstacles in images. In traditional sparse representation image restoration algorithms, the size of dictionary atoms is often fixed. When repairing the texture area, the dictionary atom will be too large to cause blurring. When repairing a smooth area, the dictionary atom is too small to cause the extension of the area, which affects the image repair effect. In this paper, the structural sparsity of the block to be repaired is used to adjust the repair priority. By analyzing the structure information of the repair block located in different regions such as texture, edge, and smoothing, the size of the dictionary atom is adaptively determined. This paper proposes a color image restoration method that adaptively determines the size of dictionary atoms and discusses a model based on the partial differential equation restoration method. Through simulation experiments combined with subjective and objective standards, the repair results are evaluated and analyzed. The simulation results show that the algorithm can effectively overcome the shortcomings of blurred details and region extension in fixed dictionary restoration, and the restoration effect has been significantly improved. Compared with the results of several other classic algorithms, it shows the effectiveness of the algorithm in this paper.

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Section: Description Of Image Repair Problemmentioning

confidence: 99%

Application of Adaptive Image Restoration Algorithm Based on Sparsity of Block Structure in Environmental Art Design

2021

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“…Visual attention mechanism aims to enhance the task-relevant feature representations of a network, and has been successfully applied to various computer vision tasks, such as image or video classification [24]- [27], semantic segmentation [28]- [30], [39], [40], video object segmentation [41]- [45], human parsing [46]- [48] ,image inpainting [49]- [51], single image super-resolution [31]- [33], [52], [53] and so on. There are some representative attention blocks as follows.…”

Section: B Visual Attention Mechanismmentioning

confidence: 99%

Light Field Image Super-Resolution via Mutual Attention Guidance

Wang

2021

IEEE Access

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Deep learning-based methods have prompted light field image super-resolution to achieve significant progress. However, most of them ignore aligning different sub-aperture features of light field image before aggregation, resulting in sub-optimal super-resolution results. We aim to propose an efficient feature alignment method for sub-aperture feature aggregation. To this end, we develop a mutual attention mechanism for sub-aperture feature alignment and propose a mutual attention guidance block (MAG). MAG achieves the mutual attention mechanism between the center feature and surrounding feature with the center attention guidance module (CAG) and the surrounding attention guidance module (SAG). CAG aligns the center-view feature with the surrounding-view feature and generates the refined surrounding-view feature, while SAG aligns the refined surrounding-view feature with the original surrounding-view feature to implement bidirectional center-view, and surrounding view features alignment. Based on MAG, we build a Light Field Mutual Attention Guidance Network (LF-MAGNet) constructed by multiple MAGs in a cascade manner. Experiments are performed on commonly-used light field image super-resolution benchmarks. Our method achieves superior qualitative and quantitative results to other state-of-the-art methods, which demonstrate the effectiveness of our LF-MAGNet.INDEX TERMS Light-field image super-resolution, Visual attention mechanism, Feature alignment.

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“…Dong et al (2016a) first proposed a three-layer convolutional neural network super-resolution method (SRCNN), which jointly optimizes the three stages of feature extraction, nonlinear mapping and image reconstruction in an end-to-end manner, demonstrating that the convolutional neural network can learn the mapping from LR to HR in an end-to-end manner [8]. Shi et al (2016) proposed an effective sub-pixel convolutional neural network (ESPCNN) [9].…”

Section: Introductionmentioning

confidence: 99%

A Super Resolution Algorithm Based on Attention Mechanism and SRGAN Network

Liu¹,

Chen²

2021

IEEE Access

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Image super-resolution reconstruction uses a specific algorithm to restore the low resolution blurred image in the same scene to a high resolution image. In recent years, with the vigorous development of deep learning, this technology has been widely used in many fields. In the field of image super-resolution reconstruction, more and more methods based on deep learning have been studied. According to the principle of GAN, a pseudo high-resolution image is generated by the generator, and then the discriminator calculates the difference between the image and the real high-resolution image to measure the authenticity of the image. Based on SRGAN (super resolution general adverse network), this paper mainly makes three improvements. First, it introduces the attention channel mechanism, that is, it adds Ca (channel attention) module to SRGAN network, and increases the network depth to better express high frequency features; Second, delete the original BN (batch normalization) layer to improve the network performance; Third, modify the loss function to reduce the impact of noise on the image. The experimental results show that the proposed method is superior to the current methods in both quantitative and qualitative indicators, and promotes the recovery of high-frequency detail information. The experimental results show that the proposed method improves the artifact problem and improves the PSNR (peak signal-to-noise ratio) on set5, set10 and bsd100 test sets.

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Research on image Inpainting algorithm of improved GAN based on two-discriminations networks

Cited by 54 publications

References 32 publications

Application of Adaptive Image Restoration Algorithm Based on Sparsity of Block Structure in Environmental Art Design

Application of Adaptive Image Restoration Algorithm Based on Sparsity of Block Structure in Environmental Art Design

Light Field Image Super-Resolution via Mutual Attention Guidance

A Super Resolution Algorithm Based on Attention Mechanism and SRGAN Network

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