Scale adaptive and lightweight super-resolution with a selective hierarchical residual network
Authors: 
Jiawang Dan, Zhaowei Qu, Xiaoru Wang, Fu Li, Jiahang Gu, Bing MaAuthors Info & Claims
Pages 8 - 14
Abstract
Deep convolutional neural networks have made remarkable achievements in single-image super-resolution tasks in recent years. However, current methods do not consider the characteristics of super-resolution that the adjacent areas carry similar information. In this paper, we propose a scale adaptive and lightweight super-resolution with a selective hierarchical residual network (SHRN), which utilizes the repeated texture features. Specifically, SHRN is stacked by several selective hierarchical residual blocks (SHRB). The SHRB mainly contains a hierarchical feature fusion structure (HFFS) and a selective feature fusion structure (SFFS). The HFFS uses multiple branches to obtain multiscale features due to the varying texture size of objects. The SFFS fuses features of adjacent branches to select effective information. Plenty of experiments demonstrate that our lightweight model achieves better performance against other methods by extracting scale adaptive features and utilizing the repeated texture structure.
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March 2021
246 pages
ISBN:9781450388634
DOI:10.1145/3461353
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Published: 04 September 2021
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ICIAI 2021: 2021 the 5th International Conference on Innovation in Artificial Intelligence
March 5 - 8, 2021
Xia men, China
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