Unpaired Learning for Deep Image Deraining with Rain Direction Regularizer

Abstract

We present a simple yet effective unpaired learning based image rain removal method from an unpaired set of synthetic images and real rainy images by exploring the properties of rain maps. The proposed algorithm mainly consists of a semi-supervised learning part and a knowledge distillation part. The semi-supervised part estimates the rain map and reconstructs the derained image based on the well-established layer separation principle. To facilitate rain removal, we develop a rain direction regularizer to constrain the rain estimation network in the semi-supervised learning part. With the estimated rain maps from the semi-supervised learning part, we first synthesize a new paired set by adding to rain-free images based on the superimposition model. The real rainy images and the derained results constitute another paired set. Then we develop an effective knowledge distillation method to explore such two paired sets so that the deraining model in the semi-supervised learning part is distilled. We propose two new rainy datasets, named RainDirection and Real3000, to validate the effectiveness of the proposed method. Both quantitative and qualitative experimental results demonstrate that the proposed method achieves favorable results against state-of-the-art methods in benchmark datasets and real-world images.