Image Deraining via Self-supervised Reinforcement Learning

• URL: http://arxiv.org/abs/2403.18270v1
• Date: Wed, 27 Mar 2024 05:52:39 GMT
• Title: Image Deraining via Self-supervised Reinforcement Learning
• Authors: He-Hao Liao, Yan-Tsung Peng, Wen-Tao Chu, Ping-Chun Hsieh, Chung-Chi Tsai,
• Abstract summary: The work aims to recover rain images by removing rain streaks via Self-supervised Reinforcement Learning (RL) We locate rain streak pixels from the input rain image via dictionary learning and use pixel-wise RL agents to take multiple inpainting actions to remove rain progressively. Experimental results on several benchmark image-deraining datasets show that the proposed SRL-Derain performs favorably against state-of-the-art few-shot and self-supervised deraining and denoising methods.
• Score: 15.41116945679692
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: The quality of images captured outdoors is often affected by the weather. One factor that interferes with sight is rain, which can obstruct the view of observers and computer vision applications that rely on those images. The work aims to recover rain images by removing rain streaks via Self-supervised Reinforcement Learning (RL) for image deraining (SRL-Derain). We locate rain streak pixels from the input rain image via dictionary learning and use pixel-wise RL agents to take multiple inpainting actions to remove rain progressively. To our knowledge, this work is the first attempt where self-supervised RL is applied to image deraining. Experimental results on several benchmark image-deraining datasets show that the proposed SRL-Derain performs favorably against state-of-the-art few-shot and self-supervised deraining and denoising methods.

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