Detail-recovery Image Deraining via Dual Sample-augmented Contrastive Learning

The intricacy of rainy image contents often leads cutting-edge deraining models to image degradation including remnant rain, wrongly-removed details, and distorted appearance. Such degradation is further exacerbated when applying the models trained on synthetic data to real-world rainy images. We observe two types of domain gaps between synthetic and real-world rainy images: one exists in rain streak patterns; the other is the pixel-level appearance of rain-free images. To bridge the two domain gaps, we propose a semi-supervised detail-recovery image deraining network (Semi-DRDNet) with dual sample-augmented contrastive learning. Semi-DRDNet consists of three sub-networks:i) for removing rain streaks without remnants, we present a squeeze-and-excitation based rain residual network; ii) for encouraging the lost details to return, we construct a structure detail context aggregation based detail repair network; to our knowledge, this is the first time; and iii) for building efficient contrastive constraints for both rain streaks and clean backgrounds, we exploit a novel dual sample-augmented contrastive regularization network.Semi-DRDNet operates smoothly on both synthetic and real-world rainy data in terms of deraining robustness and detail accuracy. Comparisons on four datasets including our established Real200 show clear improvements of Semi-DRDNet over fifteen state-of-the-art methods. Code and dataset are available at https://github.com/syy-whu/DRD-Net.Comment: 17 page

Single-Image Deraining via Recurrent Residual Multiscale Networks.

Existing deraining approaches represent rain streaks with different rain layers and then separate the layers from the background image. However, because of the complexity of real-world rain, such as various densities, shapes, and directions of rain streaks, it is very difficult to decompose a rain image into clean background and rain layers. In this article, we develop a novel single-image deraining method based on residual multiscale pyramid to mitigate the difficulty of rain image decomposition. To be specific, we progressively remove rain streaks in a coarse-to-fine fashion, where heavy rain is first removed in coarse-resolution levels and then light rain is eliminated in fine-resolution levels. Furthermore, based on the observation that residuals between a restored image and its corresponding rain image give critical clues of rain streaks, we regard the residuals as an attention map to remove rains in the consecutive finer level image. To achieve a powerful yet compact deraining framework, we construct our network by recurrent layers and remove rain with the same network in different pyramid levels. In addition, we design a multiscale kernel selection network (MSKSN) to facilitate our single network to remove rain streaks at different levels. In this manner, we reduce 81% of the model parameters without decreasing deraining performance compared with our prior work. Extensive experimental results on widely used benchmarks show that our approach achieves superior deraining performance compared with the state of the art