ContourletNet: A Generalized Rain Removal Architecture Using Multi-Direction Hierarchical Representation

TL;DR

ContourletNet is a novel hierarchical network that effectively removes rain streaks and veiling effects from images using contourlet transform, unifying moderate and heavy rain scenarios in a single architecture.

Contribution

It introduces the first unified architecture leveraging contourlet transform for rain removal across different rainy conditions.

Findings

Effective removal of rain streaks and veiling effects

Unified approach for moderate and heavy rain scenes

Improved image visibility in rainy conditions

Abstract

Images acquired from rainy scenes usually suffer from bad visibility which may damage the performance of computer vision applications. The rainy scenarios can be categorized into two classes: moderate rain and heavy rain scenes. Moderate rain scene mainly consists of rain streaks while heavy rain scene contains both rain streaks and the veiling effect (similar to haze). Although existing methods have achieved excellent performance on these two cases individually, it still lacks a general architecture to address both heavy rain and moderate rain scenarios effectively. In this paper, we construct a hierarchical multi-direction representation network by using the contourlet transform (CT) to address both moderate rain and heavy rain scenarios. The CT divides the image into the multi-direction subbands (MS) and the semantic subband (SS). First, the rain streak information is retrieved to the MS based on the multi-orientation property of the CT. Second, a hierarchical architecture is proposed to reconstruct the background information including damaged semantic information and the veiling effect in the SS. Last, the multi-level subband discriminator with the feedback error map is proposed. By this module, all subbands can be well optimized. This is the first architecture that can address both of the two scenarios effectively. The code is available in https://github.com/cctakaet/ContourletNet-BMVC2021.