GRAN: Ghost Residual Attention Network for Single Image Super Resolution

• URL: http://arxiv.org/abs/2302.14557v2
• Date: Thu, 2 Mar 2023 02:01:19 GMT
• Title: GRAN: Ghost Residual Attention Network for Single Image Super Resolution
• Authors: Axi Niu, Pei Wang, Yu Zhu, Jinqiu Sun, Qingsen Yan, Yanning Zhang
• Abstract summary: This paper introduces Ghost Residual Attention Block (GRAB) groups to overcome the drawbacks of the standard convolutional operation. Ghost Module can reveal information underlying intrinsic features by employing linear operations to replace the standard convolutions. Experiments conducted on the benchmark datasets demonstrate the superior performance of our method in both qualitative and quantitative.
• Score: 44.4178326950426
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: 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, we achieve higher performance with lower computational resources, whose parameters and FLOPs have decreased by more than ten times.

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