Image Deraining via Self-supervised Reinforcement Learning
This addresses image quality degradation due to rain for computer vision systems, but it is incremental as it applies a known RL approach to a specific domain.
The paper tackles the problem of removing rain streaks from images to improve quality for outdoor computer vision applications, proposing a self-supervised reinforcement learning method that achieves favorable performance against state-of-the-art methods on benchmark datasets.
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.