Survey on Single Image based Super-resolution — Implementation Challenges and Solutions

Singh, Amanjot; Singh, Jagroop

doi:10.1007/s11042-019-08254-0

Cited by 29 publications

(4 citation statements)

References 111 publications

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“…Yang et al proposed the Patch Match method, which uses EM to estimate the value of pixels and then uses a block matching strategy to find similar sample blocks [18]. Singh et al use multiresolution features for image restoration, first on low-resolution images and then on high-resolution images, and optimize the restoration order for single-layer images [19]. ere are many similar restoration processing methods.…”

Section: Related Workmentioning

confidence: 99%

Artistic Color Virtual Reality Implementation Based on Similarity Image Restoration

Zhu

2021

Complexity

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In this paper, exploratory and innovative research is done on the implementation technique of artistic color virtual reality for similarity image recovery. Based on similarity images, a nonlocal natural image before the regular term is proposed to deal with the single-image blind deblurring problem. This paper designs a new artistic color virtual reality realization technology based on similarity image restoration, which exploits the low-rank property between nonlocal similarity blocks in images and combines a strong convex term to enhance the convexity of the artistic color virtual reality model. We analyze virtual reality interaction design from the perspective of art color design, sort out the concept and content of design, analyze the elements, design principles, and evaluation criteria included in virtual reality interaction art color design, and explore the conceptual principles of virtual reality interaction art color design. A full understanding of the characteristics of the medium of the virtual reality interaction can help us to better use this medium as a tool to create works that aim to bring higher quality and experiential feeling with a perceptual communication method that is closer to natural interaction. Combining the power of technology, artistic colourful thinking, and a design approach paves the way forward. The study shows that virtual reality technology can effectively improve the status quo and promote the cultivation of professional practice ability in art color design, which is conducive to the cultivation of applied design talents.

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Section: Related Workmentioning

confidence: 99%

Artistic Color Virtual Reality Implementation Based on Similarity Image Restoration

Zhu

2021

Complexity

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“…With the wide application of deep learning technology in various fields [10][11][12][13][14][15], methods based on neural networks have gradually become the mainstream solution to the image super-resolution, which can solve SISR problem by implicitly learning the complex nonlinear-LR-to-HR-mapping-relationship based on numerous LR-HR image pairs [16]. SRCNN [17] is the first one using deep learning technology to solve the SISR problem.…”

Section: Introductionmentioning

confidence: 99%

GRAN: Ghost Residual Attention Network for Single Image Super Resolution

Niu¹,

Wang²,

Zhu³

et al. 2023

Preprint

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Recently, many works have designed wider and deeper networks to achieve higher image super-resolution performance. Despite their outstanding performance, they still suffer from high computational resources, preventing them from directly applying to embedded devices. To reduce the computation resources and maintain performance, we propose a novel Ghost Residual Attention Network (GRAN) for efficient super-resolution. This paper introduces Ghost Residual Attention Block (GRAB) groups to overcome the drawbacks of the standard convolutional operation, i.e., redundancy of the intermediate feature. GRAB consists of the Ghost Module and Channel and Spatial Attention Module (CSAM) to alleviate the generation of redundant features. Specifically, Ghost Module can reveal information underlying intrinsic features by employing linear operations to replace the standard convolutions. Reducing redundant features by the Ghost Module, our model decreases memory and computing resource requirements in the network. The CSAM pays more comprehensive attention to where and what the feature extraction is, which is critical to recovering the image details. Experiments conducted on the benchmark datasets demonstrate the superior performance of our method in both qualitative and quantitative. Compared to the baseline models,

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“…The super-resolution of 2D images is primarily studied and widespread in many applications, such as biomedicine [LAPB14], astronomy [PK05], and industrial [QCJY22] context. In the literature, several methods guarantee excellent results in terms of reconstruction accuracy [SS20]. However, most of the current super-resolution methods do not account for noise in the image.…”

Section: Introductionmentioning

confidence: 99%

Learning-based low-rank denoising

Cammarasana

Patanè

2022

SIViP

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The denoising of 2D images through low-rank methods is a relevant topic in digital image processing. This paper proposes a novel method that trains a learning network to predict the optimal thresholds of the singular value decomposition involved in the low-rank denoising of 2D images. To improve the denoising results, we apply the block-matching algorithm and classify each 3D block according to four parameters, which increase the specificity of the network for the prediction of the thresholds. Our method outperforms state-of-the-art methods for image denoising; furthermore, it is general with respect to the type of noise and provides an upper bound to the accuracy of the denoising of 2D images through the Singular Value Decomposition.

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Survey on Single Image based Super-resolution — Implementation Challenges and Solutions

Cited by 29 publications

References 111 publications

Artistic Color Virtual Reality Implementation Based on Similarity Image Restoration

Artistic Color Virtual Reality Implementation Based on Similarity Image Restoration

GRAN: Ghost Residual Attention Network for Single Image Super Resolution

Learning-based low-rank denoising

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