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

arXiv: https://arxiv.org/abs/2111.12925 · HTML: https://mlsanity.com/paper/2111.12925/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 17 of its 38 references that are in the map.
- First posted on arXiv 25 Nov 2021.

## Builds on (most cited first, 17 of 17)

- [Adam: A Method for Stochastic Optimization](https://mlsanity.com/paper/1412.6980/) (2014)
- [Image-to-Image Translation with Conditional Adversarial Networks](https://mlsanity.com/paper/1611.07004/) (2016)
- [Perceptual Losses for Real-Time Style Transfer and Super-Resolution](https://mlsanity.com/paper/1603.08155/) (2016)
- [Accurate Image Super-Resolution Using Very Deep Convolutional Networks](https://mlsanity.com/paper/1511.04587/) (2015)
- [Res2Net: A New Multi-scale Backbone Architecture](https://mlsanity.com/paper/1904.01169/) (2019)
- [Deep Joint Rain Detection and Removal from a Single Image](https://mlsanity.com/paper/1609.07769/) (2016)
- [Progressive Image Deraining Networks: A Better and Simpler Baseline](https://mlsanity.com/paper/1901.09221/) (2019)
- [Multi-Scale Progressive Fusion Network for Single Image Deraining](https://mlsanity.com/paper/2003.10985/) (2020)
- [Multi-Scale Boosted Dehazing Network with Dense Feature Fusion](https://mlsanity.com/paper/2004.13388/) (2020)
- [Image De-raining Using a Conditional Generative Adversarial Network](https://mlsanity.com/paper/1701.05957/) (2017)
- [Spatial Attentive Single-Image Deraining with a High Quality Real Rain Dataset](https://mlsanity.com/paper/1904.01538/) (2019)
- [Heavy Rain Image Restoration: Integrating Physics Model and Conditional Adversarial Learning](https://mlsanity.com/paper/1904.05050/) (2019)
- [Fast and Accurate Image Super-Resolution with Deep Laplacian Pyramid Networks](https://mlsanity.com/paper/1710.01992/) (2017)
- [A Model-driven Deep Neural Network for Single Image Rain Removal](https://mlsanity.com/paper/2005.01333/) (2020)
- [Lightweight Pyramid Networks for Image Deraining](https://mlsanity.com/paper/1805.06173/) (2018)
- [Syn2Real Transfer Learning for Image Deraining using Gaussian Processes](https://mlsanity.com/paper/2006.05580/) (2020)
- [Wavelet Channel Attention Module with a Fusion Network for Single Image Deraining](https://mlsanity.com/paper/2007.09163/) (2020)

## Built on by (most cited first, 1 of 1)

- [NTIRE 2022 Challenge on Stereo Image Super-Resolution: Methods and Results](https://mlsanity.com/paper/2204.09197/) (2022)

## Abstract (arXiv)

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.