Rain2Avoid: Learning Deraining by Self-Supervision

Images captured on rainy days often contain rain streaks that can obscure important scenery and degrade the performance of high-level vision tasks, such as image segmentation in autonomous vehicles. As a result, image deraining, a low-level vision task focused on removing rain streaks from images, has gained popularity over the past decade. Recent advancements have primarily concentrated on supervised image deraining methods, which rely on paired rain-clean image datasets to train deep neural network models. However, collecting such paired real data is challenging and time-consuming. To address this, our method introduces a novel self-supervised approach that leverages the proposed locally dominant gradient prior and non-local self-similarity stochastic sampling. This approach extracts potential rain streaks and generates stochastic derained references for image deraining. Experimental results on public benchmark image-deraining datasets show that our proposed method performs favorably against state-of-the-art few-shot and self-supervised image deraining methods.