Rain removal via residual generation cascading
Single-image rain removal has always been challenging, due to its inherent ill-posed nature. In this paper, we propose a novel rain removal pipeline. This pipeline features two processing pathways: 1) a bottom-up pathway which iteratively performs down sampling and subtraction operations, yielding a Laplacian pyramid (LP) with decreasing scales, to bypass rain details; and 2) a top-down pathway which is built on image series obtained by the above bottom-up process and generate a series of image components based on conditional generative adversarial nets (CGAN), aiming at removing rain streaks while preserving image details. Generated components are further fused via scale-space aggregation, and descriptive image intrinsic information contained in different scales is interacted to jointly generate high quality de-rained image. Extensive experiments on synthetic and real-world rainy image datasets both demonstrate the effectiveness of the proposed rain streaks removal approach.