ClearMamba: A State Space Model with Global Context Fusion for Single Image Deraining

YAO Qi-you; HAO Juan; LIU Xiao-qun; SUN Wei-kai; XI Hai-lin; WU Yu-wen

Computer & Telecommunication ›› 2025, Vol. 1 ›› Issue (9) : 21-26.

YAO Qi-you, HAO Juan^*, LIU Xiao-qun, SUN Wei-kai, XI Hai-lin, WU Yu-wen

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Abstract

To address the importance of global background information and local detail features in rain streak removal, as well as the limitations of deep learning models in handling high-resolution images, we propose ClearMamba, a spatio-temporal state-space model with global context fusion. This model achieves pixel-level feature refinement through spatio-temporal feature modeling and global context integration, significantly enhancing single-image deraining performance. Experiments on datasets such as DID-Data demonstrate the superiority of this algorithm, with a peak signal-to-noise ratio (PSNR) improvement of approximately 4.9% compared to methods like PReNet. Furthermore, we systematically evaluate the empowering potential of deraining models for downstream visual tasks (e.g., object detection), achieving top performance. The proposed method offers a practical solution for optimizing intelligent visual systems in rainy environments.

Key words

global background information / local detail features / rain streak removal / deep learning model / spatio-temporal state-space model / pixel-level feature refinement / downstream visual tasks

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YAO Qi-you, HAO Juan, LIU Xiao-qun, SUN Wei-kai, XI Hai-lin, WU Yu-wen. ClearMamba: A State Space Model with Global Context Fusion for Single Image Deraining[J]. Computer & Telecommunication. 2025, 1(9): 21-26

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