Single image rain streaks removal based on self-learning and structured sparse representation

Rain streaks removal from single image is a challenging problem for image processing. This paper proposed a novel algorithm for rain streaks removal to single image based on a self-learning framework and structured sparse representation. More precisely, our algorithm firstly segments and categorizes input image into “rain streaks” regions and “non-rain geometric” regions via texture analysis. Meanwhile, we also decompose input image into high-frequency (HF) and low-frequency (LF) parts with bilateral filtering. Followed that, we introduced our newly proposed structured dictionary learning to decompose HF part into “rain texture” details and “non-rain geometric” details, where patches for training rain and non-rain sub-dictionaries are automatically selected from “rain streaks” and “non-rain geometric” regions. Finally, we combine LF part with non-rain geometric details to get rain-streaks-removal image. Experiments demonstrate the superiority of our proposed algorithm.