Removing rain from a single image via discriminative sparse coding∗

Visual distortions on images caused by bad weather con-ditions can have a negative impact on the performance of many outdoor vision systems. One often seen bad weather is rain which causes significant yet complex local intensi-ty fluctuations in images. The paper aims at developing an effective algorithm to remove visual effects of rain from a single rain image, i.e. separate the rain layer and the de-rained image layer from a rain image. Built upon a non-linear generative model of rain image, namely screen blend model, we propose a dictionary learning based algorithm for single image de-raining. The basic idea is to sparsely approximate the patches of two layers by very high discrim-inative codes over a learned dictionary with strong mutual exclusivity property. Such discriminative sparse codes lead to accurate separation of two layers from their non-linear composite. The experiments show that the proposed method outperforms the existing single image de-raining methods on tested rain images. 1