Double-Layer Search and Adaptive Pooling Fusion for Reference-Based Image Super-Resolution
Authors: 
Kehua Guo, Liang Chen, Xiangyuan Zhu, Xiaoyan Kui, Jian Zhang, Heyuan ShiAuthors Info & Claims
Kehua Guo, Liang Chen, Xiangyuan Zhu, Xiaoyan Kui, Jian Zhang, Heyuan ShiAuthors Info & ClaimsArticle No.: 15, Pages 1 - 23
Abstract
Reference-based image super-resolution (RefSR) aims to reconstruct high-resolution (HR) images from low-resolution (LR) images by introducing HR reference images. The key step of RefSR is to transfer reference features to LR features. However, existing methods still lack an efficient transfer mechanism, resulting in blurry details in the generated image. In this article, we propose a double-layer search module and an adaptive pooling fusion module group for reference-based image super-resolution, called DLASR. Based on the re-search strategy, the double-layer search module can produce an accurate index map and score map. These two maps are used to filter out accurate reference features, which greatly increases the efficiency of feature transfer in the later stage. Through two continuous feature-enhancement steps, the adaptive pooling fusion module group can transfer more valuable reference features to the corresponding LR features. In addition, a structure reconstruction module is proposed to recover the geometric information of the images, which further improves the visual quality of the generated image. We conduct comparative experiments on a variety of datasets, and the results prove that DLASR achieves significant improvements over other state-of-the-art methods, in terms of quantitative accuracy and qualitative visual effect. The code is available at https://github.com/clttyou/DLASR.
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Index Terms
- Double-Layer Search and Adaptive Pooling Fusion for Reference-Based Image Super-Resolution
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January 2024
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Publication History
Published: 25 August 2023
Online AM: 21 June 2023
Accepted: 13 June 2023
Revised: 07 May 2023
Received: 07 October 2022
Published in TOMM Volume 20, Issue 1
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- Natural Science Foundation of China
- Hunan Provincial Science and Technology Plan Project
- National Science Foundation of Hunan Province, China
- National Social Science Fund of China
- Fundamental Research Funds for the Central Universities of the Central South University
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