Image super-resolution reconstruction based on feature map attention mechanism

Chen, Yuantao; Liu, Linwu; Phonevilay, Volachith; Gu, Ke; Xia, Runlong; Xie, Jiang; Zhang, Qian; Yang, Kai

doi:10.1007/s10489-020-02116-1

Cited by 190 publications

(102 citation statements)

References 21 publications

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“…Similarly, the non-local attention technique was incorporated by Dai et al (2019) to capture contextual information using a non-local attention method. Chen et al proposed an SR reconstruction method with feature maps to facilitate the reconstruction of the image using an attention mechanism ( Chen et al, 2021 ), while Yang et al proposed a channel attention and spatial graph convolutional network (CASGCN) for a more robust feature obtaining and feature correlations modeling ( Yang & Qi, 2021 ).…”

Section: Supervised Super-resolutionmentioning

confidence: 99%

A comprehensive review of deep learning-based single image super-resolution

Bashir

Wang

Khan

et al. 2021

PeerJ Computer Science

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Image super-resolution (SR) is one of the vital image processing methods that improve the resolution of an image in the field of computer vision. In the last two decades, significant progress has been made in the field of super-resolution, especially by utilizing deep learning methods. This survey is an effort to provide a detailed survey of recent progress in single-image super-resolution in the perspective of deep learning while also informing about the initial classical methods used for image super-resolution. The survey classifies the image SR methods into four categories, i.e., classical methods, supervised learning-based methods, unsupervised learning-based methods, and domain-specific SR methods. We also introduce the problem of SR to provide intuition about image quality metrics, available reference datasets, and SR challenges. Deep learning-based approaches of SR are evaluated using a reference dataset. Some of the reviewed state-of-the-art image SR methods include the enhanced deep SR network (EDSR), cycle-in-cycle GAN (CinCGAN), multiscale residual network (MSRN), meta residual dense network (Meta-RDN), recurrent back-projection network (RBPN), second-order attention network (SAN), SR feedback network (SRFBN) and the wavelet-based residual attention network (WRAN). Finally, this survey is concluded with future directions and trends in SR and open problems in SR to be addressed by the researchers.

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Section: Supervised Super-resolutionmentioning

confidence: 99%

A comprehensive review of deep learning-based single image super-resolution

Bashir

Wang

Khan

et al. 2021

PeerJ Computer Science

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“…(Dai et al 2019) uses second-order channels to enhance feature expression and feature learning to capture long-distance context space information. (Chen et al 2021) Propose an image super-resolution reconstruction method using attention mechanism with feature map to improve the low-frequency and high-frequency components of feature map. In this study, we propose a feature extraction network based on attention mechanism to improve the performance of high-frequency feature extraction.…”

Section: Attention Mechanismmentioning

confidence: 99%

Image Super Resolution Reconstruction Based on Improved Invertible Rescaling Net

Chen

2021

Preprint

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The Invertible Rescaling Net (IRN) is modeling image downscaling and upscaling as a unified task to alleviate the ill-posed problem in the super-resolution task. However, the ability of potential variables of the model embedded high-frequency information is general, which affects the performance of the reconstructed image. In order to improve the ability of embedding high-frequency information and further reduce the complexity of the model, the potential variables and feature extraction of key components of IRN are improved. Attention mechanism and dilated convolution are used to improve the feature extraction block, reduce the parameters of feature extraction block, and allocate more attention to the image details. The high frequency sub-band interpolation method of wavelet domain is used to improve the potential variables, process and save the image edge, and enhance the ability of embedding high frequency information. Experimental results show that compared with IRN model, improved model has less complexity and excellent performance.

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“…In [15], the targeted perceptual loss was used to improve the perceptual quality of the super-resolution results. [62]…”

Section: Related Workmentioning

confidence: 99%

Reliable Perceptual Loss Computation for GAN-Based Super-Resolution With Edge Texture Metric

Kim

Lee

2021

IEEE Access

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Super-resolution (SR) is an ill-posed problem. Generating high-resolution (HR) images from low-resolution (LR) images remains a major challenge. Recently, SR methods based on deep convolutional neural networks (DCN) have been developed with impressive performance improvement. DCN-based SR techniques can be largely divided into peak signal-to-noise ratio (PSNR)-oriented SR networks and generative adversarial networks (GAN)-based SR networks. In most current GAN-based SR networks, the perceptual loss is computed from the feature maps of a single layer or several fixed layers using a differentiable feature extractor such as VGG. This limited layer utilization may produce overly textured artifacts. In this paper, a new edge texture metric (ETM) is proposed to quantify the characteristics of images and then it is utilized only in the training phase to select an appropriate layer when calculating the perceptual loss. We present experimental results showing that the GAN-based SR network trained with the proposed method achieves qualitative and quantitative perceptual quality improvements compared to many of the existing methods. INDEX TERMSArtificial neural networks, computer vision, image enhancement, image resolution VOLUME 4, 2016This article has been accepted for publication in a future issue of this journal, but has not been fully edited. Content may change prior to final publication.

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Image super-resolution reconstruction based on feature map attention mechanism

Cited by 190 publications

References 21 publications

A comprehensive review of deep learning-based single image super-resolution

A comprehensive review of deep learning-based single image super-resolution

Image Super Resolution Reconstruction Based on Improved Invertible Rescaling Net

Reliable Perceptual Loss Computation for GAN-Based Super-Resolution With Edge Texture Metric

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