# Online-updated High-order Collaborative Networks for Single Image Deraining

arXiv: https://arxiv.org/abs/2202.06568 · HTML: https://mlsanity.com/paper/2202.06568/

## Key facts

- Cited by 13 papers in the map (data through 14 Oct 2026).
- Builds on 18 of its 32 references that are in the map.
- First posted on arXiv 14 Feb 2022.

## Builds on (most cited first, 18 of 18)

- [Adam: A Method for Stochastic Optimization](https://mlsanity.com/paper/1412.6980/) (2014)
- [Multi-Stage Progressive Image Restoration](https://mlsanity.com/paper/2102.02808/) (2021)
- [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)
- [Density-aware Single Image De-raining using a Multi-stream Dense Network](https://mlsanity.com/paper/1802.07412/) (2018)
- [Recurrent Squeeze-and-Excitation Context Aggregation Net for Single Image Deraining](https://mlsanity.com/paper/1807.05698/) (2018)
- [Spatial Attentive Single-Image Deraining with a High Quality Real Rain Dataset](https://mlsanity.com/paper/1904.01538/) (2019)
- [A Model-driven Deep Neural Network for Single Image Rain Removal](https://mlsanity.com/paper/2005.01333/) (2020)
- [Single Image Deraining: A Comprehensive Benchmark Analysis](https://mlsanity.com/paper/1903.08558/) (2019)
- [Syn2Real Transfer Learning for Image Deraining using Gaussian Processes](https://mlsanity.com/paper/2006.05580/) (2020)
- [DCSFN: Deep Cross-scale Fusion Network for Single Image Rain Removal](https://mlsanity.com/paper/2008.00767/) (2020)
- [Physics-Based Generative Adversarial Models for Image Restoration and Beyond](https://mlsanity.com/paper/1808.00605/) (2018)
- [Learning Dual Convolutional Neural Networks for Low-Level Vision](https://mlsanity.com/paper/1805.05020/) (2018)
- [Non-locally Enhanced Encoder-Decoder Network for Single Image De-raining](https://mlsanity.com/paper/1808.01491/) (2018)
- [Joint Self-Attention and Scale-Aggregation for Self-Calibrated Deraining Network](https://mlsanity.com/paper/2008.02763/) (2020)
- [Pyramid Attention Networks for Image Restoration](https://mlsanity.com/paper/2004.13824/) (2020)
- [Physical Model Guided Deep Image Deraining](https://mlsanity.com/paper/2003.13242/) (2020)

## Built on by (most cited first, 13 of 13)

- [FourierMamba: Fourier Learning Integration with State Space Models for Image Deraining](https://mlsanity.com/paper/2405.19450/) (2024)
- [Unpaired Photo-realistic Image Deraining with Energy-informed Diffusion Model](https://mlsanity.com/paper/2407.17193/) (2024)
- [Dual Degradation Representation for Joint Deraining and Low-Light Enhancement in the Dark](https://mlsanity.com/paper/2305.03997/) (2023)
- [Unsupervised Deraining: Where Asymmetric Contrastive Learning Meets Self-similarity](https://mlsanity.com/paper/2211.00837/) (2022)
- [Explore Internal and External Similarity for Single Image Deraining with Graph Neural Networks](https://mlsanity.com/paper/2406.00721/) (2024)
- [Neural Discrimination-Prompted Transformers for Efficient UHD Image Restoration and Enhancement](https://mlsanity.com/paper/2603.00853/) (2026)
- [Deep Learning-Driven Ultra-High-Definition Image Restoration: A Survey](https://mlsanity.com/paper/2505.16161/) (2025)
- [Harnessing Joint Rain-/Detail-aware Representations to Eliminate Intricate Rains](https://mlsanity.com/paper/2404.12091/) (2024)
- [How Powerful Potential of Attention on Image Restoration?](https://mlsanity.com/paper/2403.10336/) (2024)
- [Degradation-Aware Prompt Learning with Cross-Modal Compensation for Adverse Weather Removal](https://mlsanity.com/paper/2608.06939/) (2026)
- [Iterative Optimal Attention and Local Model for Single Image Rain Streak Removal](https://mlsanity.com/paper/2503.16165/) (2025)
- [ControlMambaIR: Conditional Controls with State-Space Model for Image Restoration](https://mlsanity.com/paper/2506.02633/) (2025)
- [Ultra-High-Definition Restoration Transformers with Correlation Matching Transformation](https://mlsanity.com/paper/2608.20263/) (2026)

## Abstract (arXiv)

Single image deraining is an important and challenging task for some downstream artificial intelligence applications such as video surveillance and self-driving systems. Most of the existing deep-learning-based methods constrain the network to generate derained images but few of them explore features from intermediate layers, different levels, and different modules which are beneficial for rain streaks removal. In this paper, we propose a high-order collaborative network with multi-scale compact constraints and a bidirectional scale-content similarity mining module to exploit features from deep networks externally and internally for rain streaks removal. Externally, we design a deraining framework with three sub-networks trained in a collaborative manner, where the bottom network transmits intermediate features to the middle network which also receives shallower rainy features from the top network and sends back features to the bottom network. Internally, we enforce multi-scale compact constraints on the intermediate layers of deep networks to learn useful features via a Laplacian pyramid. Further, we develop a bidirectional scale-content similarity mining module to explore features at different scales in a down-to-up and up-to-down manner. To improve the model performance on real-world images, we propose an online-update learning approach, which uses real-world rainy images to fine-tune the network and update the deraining results in a self-supervised manner. Extensive experiments demonstrate that our proposed method performs favorably against eleven state-of-the-art methods on five public synthetic datasets and one real-world dataset. The source code will be available at urlhttps://supercong94.wixsite.com/supercong94.