Decomposed Cyclegan for Single Image Deraining With Unpaired Data

Abstract

Most previous learning-based methods required paired rain image data. In practice, however, paired rain data cannot be collected. Inspired by adopting unpaired data in task of translation, in this paper we present a new method for rain removal using unpaired data. We noticed that direct use of unpaired training data may have problems, such as color shifts and background blurs. Thus, we formulate DCycleGAN, a new deep framework that decomposes the input rain image into the foreground and background parts, then produces a rain mask to guide the rain generation via re-formulated cycle-consistency constraints. Particular, the framework can simultaneously learn the foreground and background portions of the rain image, which can better remove the rain streak. Experimental results demonstrate the effectiveness of our method when trained on unpaired data.