Single-image de-raining using low-rank matrix approximation

Abstract

Most existing image de-raining methods are based on the assumption that rain has certain shape or chromatic priors, which may be inaccurate for some rain cases and may lead to unsatisfactory de-rained results. In this paper, the proposed image de-raining method is built on the priors of clear images that many self-similar patches exist in natural images. And similar patches satisfy the low-rank property when grouped together. This property has led to many powerful image/video denoising schemes with impressive performance but has never been used for single-image de-raining. We formulate the problem of removing rain as a low-rank matrix approximation problem by adopting this self-similarity image prior with a designed patch matching strategy applicable for rain images. The resulting nuclear norm minimization problem can be efficiently solved by many recently developed methods. Experiments on both real rain images and synthetic rain images show that the proposed method is competitive against other state-of-the-art methods.