Dual-Domain Single Image De-Raining Using Conditional Generative Adversarial Network

Abstract

This paper presents a novel method for a single image rain streak removal problem which exploits the spatial as well as wavelet transformed coefficients of the rainy images. The proposed method adopts the Conditional Generative Adversarial Network [1] framework and consists of two following networks: Generator and Discriminator. The generator model receives the input from both spatial, frequency domain of the rainy image and yields five de-rained image candidates. A Deep Residual Network [2] has been used to merge these derained candidates and predict a single de-rained image. To ensure the visual quality of the de-rained image, Perceptual loss function [3] in addition to adversarial training has been incorporated. Extensive experiments on the synthetic and realworld rainy images dataset reveal an improvement over the existing state-of-the-art methods [4], [5] by ~ 1.08%, 2.57% in Structural Similarity Index [6] and ~ 7.39%, 9.95% in Peak signal-to-noise ratio respectively.