Adaptive Residual Channel Attention Network for Single Image Super-Resolution

Cao, Kerang; Liu, Yuqing; Duan, Lini; Xie, Tian

doi:10.1155/2020/8877851

Cited by 4 publications

(2 citation statements)

References 39 publications

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“…We compare our method with 11 state-of-the-art SISR methods: SRCNN [17], VDSR [32], CARN [61], MSRN [30], RCAN [9], SISR-CA-OA [62], MLRN [31], MCAN [33], DBPN [63], HRAN [64], ARCAN [65]. These methods are some representative and outstanding methods since 2016, especially since their network structure is becoming more and more complex for improving performance.…”

Section: Comparisons With State-of-the-art Algorithmsmentioning

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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Section: Comparisons With State-of-the-art Algorithmsmentioning

confidence: 99%

GRAN: Ghost Residual Attention Network for Single Image Super Resolution

Niu¹,

Wang²,

Zhu³

et al. 2023

Preprint

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“…Image superresolution (SR), as a traditional image processing issue, is widely considered in advanced vision applications, such as medical image enhancement, slope collapse detection, and art image classification [1]. Given a low-resolution (LR) image, the task of image SR is to find a corresponding high-resolution (HR) instance with satisfactory visual experience and more correct textures [2].…”

Section: Introductionmentioning

confidence: 99%

Multilevel Feature Exploration Network for Image Superresolution

Liu

Wang²,

Chen

et al. 2022

Scientific Programming

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Image superresolution (SR) is a classical issue in computer vision area. Recently, there are elaborated convolutional neural networks (CNNs) demonstrating remarkable effectiveness on image SR. However, most of the previous works lack effective exploration on the structural information, which plays a critical role for image quality. In this paper, we find that the hierarchical design can effectively restore the structural information and devise a multilevel feature exploration network for image SR (MFSR). Specially, we design an encoder-decoder architecture to concentrate on structural information from different levels and devise a spatial attention mechanism to address the inherent correlation among features for effective restoration. Experimental results show the proposed MFSR can restore more correct edges and lines and achieves both better objective and subjective performances than the state-of-the-art methods with higher PSNR/SSIM results, indicating the effectiveness on structural information restoration.

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