Visual depth guided color image rain streaks removal using sparse coding

Rain removal from a single color image is a challenging problem as no temporal information among successive images can be obtained. In this paper, we propose a single-color-image-based rain removal framework by properly formulating rain removal as an image decomposition problem based on sparse representation. In our framework, an input color image is first decomposed into a low-frequency part and a high-frequency part by using the guided image filter so that the rain streaks would be in the high-frequency part with nonrain textures/edges, and the high-frequency part is then decomposed into a rain component and a nonrain component by performing dictionary learning and sparse coding. To separate rain streaks from the high-frequency part, a hybrid feature set, including histogram of oriented gradients, depth of field, and Eigen color, is employed to further decompose the high-frequency part. With the hybrid feature set applied, most rain streaks can be removed; simultaneously nonrain component can be enhanced. To the best of our knowledge, compared with the state-of-the-art approaches, the proposed method is among the first to focus on the problem of single color image rain removal and achieves promising results with not only the rain component being removed more completely, but also the visual quality of restored images being improved.