Image Deraining via Self-supervised Reinforcement Learning
He-Hao Liao Yan-Tsung Peng Wen-Tao Chu Ping-Chun Hsieh Chung-Chi Tsai
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.