# Channel Consistency Prior and Self-Reconstruction Strategy Based Unsupervised Image Deraining

arXiv: https://arxiv.org/abs/2503.18703 · HTML: https://mlsanity.com/paper/2503.18703/

## Key facts

- Cited by 11 papers in the map (data through 14 Oct 2026).
- Builds on 34 of its 56 references that are in the map.
- First posted on arXiv 24 Mar 2025.

## Builds on (most cited first, 30 of 34)

- [Adam: A Method for Stochastic Optimization](https://mlsanity.com/paper/1412.6980/) (2014)
- [PyTorch: An Imperative Style, High-Performance Deep Learning Library](https://mlsanity.com/paper/1912.01703/) (2019)
- [Very Deep Convolutional Networks for Large-Scale Image Recognition](https://mlsanity.com/paper/1409.1556/) (2014)
- [The Unreasonable Effectiveness of Deep Features as a Perceptual Metric](https://mlsanity.com/paper/1801.03924/) (2018)
- [Faster R-CNN: Towards Real-Time Object Detection with Region Proposal Networks](https://mlsanity.com/paper/1506.01497/) (2015)
- [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)
- [Restormer: Efficient Transformer for High-Resolution Image Restoration](https://mlsanity.com/paper/2111.09881/) (2021)
- [Uformer: A General U-Shaped Transformer for Image Restoration](https://mlsanity.com/paper/2106.03106/) (2021)
- [Multi-Stage Progressive Image Restoration](https://mlsanity.com/paper/2102.02808/) (2021)
- [Image Quality Assessment: Unifying Structure and Texture Similarity](https://mlsanity.com/paper/2004.07728/) (2020)
- [Simple Baselines for Image Restoration](https://mlsanity.com/paper/2204.04676/) (2022)
- [Deep Learning for Generic Object Detection: A Survey](https://mlsanity.com/paper/1809.02165/) (2018)
- [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)
- [Density-aware Single Image De-raining using a Multi-stream Dense Network](https://mlsanity.com/paper/1802.07412/) (2018)
- [HINet: Half Instance Normalization Network for Image Restoration](https://mlsanity.com/paper/2105.06086/) (2021)
- [Recurrent Squeeze-and-Excitation Context Aggregation Net for Single Image Deraining](https://mlsanity.com/paper/1807.05698/) (2018)
- [Learning A Sparse Transformer Network for Effective Image Deraining](https://mlsanity.com/paper/2303.11950/) (2023)
- [Spatial Attentive Single-Image Deraining with a High Quality Real Rain Dataset](https://mlsanity.com/paper/1904.01538/) (2019)
- [Clearing the Skies: A deep network architecture for single-image rain removal](https://mlsanity.com/paper/1609.02087/) (2016)
- [Single Image Deraining: A Comprehensive Benchmark Analysis](https://mlsanity.com/paper/1903.08558/) (2019)
- [Structure-Preserving Deraining with Residue Channel Prior Guidance](https://mlsanity.com/paper/2108.09079/) (2021)
- [Syn2Real Transfer Learning for Image Deraining using Gaussian Processes](https://mlsanity.com/paper/2006.05580/) (2020)
- [Unpaired Deep Image Deraining Using Dual Contrastive Learning](https://mlsanity.com/paper/2109.02973/) (2021)
- [Bidirectional Multi-Scale Implicit Neural Representations for Image Deraining](https://mlsanity.com/paper/2404.01547/) (2024)
- [Toward Real-world Single Image Deraining: A New Benchmark and Beyond](https://mlsanity.com/paper/2206.05514/) (2022)
- [Closing the Loop: Joint Rain Generation and Removal via Disentangled Image Translation](https://mlsanity.com/paper/2103.13660/) (2021)
- [Unsupervised Deraining: Where Contrastive Learning Meets Self-similarity](https://mlsanity.com/paper/2203.11509/) (2022)
- [Unsupervised Image Denoising in Real-World Scenarios via Self-Collaboration Parallel Generative Adversarial Branches](https://mlsanity.com/paper/2308.06776/) (2023)

## Built on by (most cited first, 11 of 11)

- [Learning Domain-Aware Task Prompt Representations for Multi-Domain All-in-One Image Restoration](https://mlsanity.com/paper/2603.01725/) (2026)
- [Real-World Adverse Weather Image Restoration via Dual-Level Reinforcement Learning with High-Quality Cold Start](https://mlsanity.com/paper/2511.05095/) (2025)
- [Unpaired Image Deraining Using Reward-Guided Self-Reinforcement Strategy](https://mlsanity.com/paper/2605.00719/) (2026)
- [WeatherCycle: Unpaired Multi-Weather Restoration via Color Space Decoupled Cycle Learning](https://mlsanity.com/paper/2509.23150/) (2025)
- [PRISM: Progressive Rain removal with Integrated State-space Modeling](https://mlsanity.com/paper/2509.26413/) (2025)
- [SkillIR: Evolving Scene-Aware Skills for Agentic Image Restoration](https://mlsanity.com/paper/2609.21468/) (2026)
- [TDiR: Transformer based Diffusion for Image Restoration Tasks](https://mlsanity.com/paper/2506.20302/) (2025)
- [CAWM-Mamba: A unified model for infrared-visible image fusion and compound adverse weather restoration](https://mlsanity.com/paper/2603.02560/) (2026)
- [One CT Unified Model Training Framework to Rule All Scanning Protocols](https://mlsanity.com/paper/2603.15025/) (2026)
- [Physically-Guided Optical Inversion Enable Non-Contact Side-Channel Attack on Isolated Screens](https://mlsanity.com/paper/2604.13419/) (2026)
- [DerainSplat: Feed-Forward Clean 3D Gaussian Splatting from Sparse Rainy Views](https://mlsanity.com/paper/2608.02191/) (2026)

## Abstract (arXiv)

Recently, deep image deraining models based on paired datasets have made a series of remarkable progress. However, they cannot be well applied in real-world applications due to the difficulty of obtaining real paired datasets and the poor generalization performance. In this paper, we propose a novel Channel Consistency Prior and Self-Reconstruction Strategy Based Unsupervised Image Deraining framework, CSUD, to tackle the aforementioned challenges. During training with unpaired data, CSUD is capable of generating high-quality pseudo clean and rainy image pairs which are used to enhance the performance of deraining network. Specifically, to preserve more image background details while transferring rain streaks from rainy images to the unpaired clean images, we propose a novel Channel Consistency Loss (CCLoss) by introducing the Channel Consistency Prior (CCP) of rain streaks into training process, thereby ensuring that the generated pseudo rainy images closely resemble the real ones. Furthermore, we propose a novel Self-Reconstruction (SR) strategy to alleviate the redundant information transfer problem of the generator, further improving the deraining performance and the generalization capability of our method. Extensive experiments on multiple synthetic and real-world datasets demonstrate that the deraining performance of CSUD surpasses other state-of-the-art unsupervised methods and CSUD exhibits superior generalization capability.