Mutual Guidance and Residual Integration for Image Enhancement

• URL: http://arxiv.org/abs/2211.13919v1
• Date: Fri, 25 Nov 2022 06:12:39 GMT
• Title: Mutual Guidance and Residual Integration for Image Enhancement
• Authors: Kun Zhou, KenKun Liu, Wenbo Li, Xiaoguang Han, Jiangbo Lu
• Abstract summary: We propose a novel mutual guidance network (MGN) to perform effective bidirectional global-local information exchange. In our design, we adopt a two-branch framework where one branch focuses more on modeling global relations while the other is committed to processing local information. As a result, both the global and local branches can enjoy the merits of mutual information aggregation.
• Score: 43.282397174228116
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Previous studies show the necessity of global and local adjustment for image enhancement. However, existing convolutional neural networks (CNNs) and transformer-based models face great challenges in balancing the computational efficiency and effectiveness of global-local information usage. Especially, existing methods typically adopt the global-to-local fusion mode, ignoring the importance of bidirectional interactions. To address those issues, we propose a novel mutual guidance network (MGN) to perform effective bidirectional global-local information exchange while keeping a compact architecture. In our design, we adopt a two-branch framework where one branch focuses more on modeling global relations while the other is committed to processing local information. Then, we develop an efficient attention-based mutual guidance approach throughout our framework for bidirectional global-local interactions. As a result, both the global and local branches can enjoy the merits of mutual information aggregation. Besides, to further refine the results produced by our MGN, we propose a novel residual integration scheme following the divide-and-conquer philosophy. The extensive experiments demonstrate the effectiveness of our proposed method, which achieves state-of-the-art performance on several public image enhancement benchmarks.

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