# Unsupervised Image Denoising in Real-World Scenarios via Self-Collaboration Parallel Generative Adversarial Branches

arXiv: https://arxiv.org/abs/2308.06776 · HTML: https://mlsanity.com/paper/2308.06776/

## Key facts

- Cited by 29 papers in the map (data through 14 Oct 2026).
- Builds on 30 of its 51 references that are in the map.
- First posted on arXiv 13 Aug 2023.

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

- [U-Net: Convolutional Networks for Biomedical Image Segmentation](https://mlsanity.com/paper/1505.04597/) (2015)
- [Beyond a Gaussian Denoiser: Residual Learning of Deep CNN for Image Denoising](https://mlsanity.com/paper/1608.03981/) (2016)
- [Simple Baselines for Image Restoration](https://mlsanity.com/paper/2204.04676/) (2022)
- [Unpaired Image-to-Image Translation using Cycle-Consistent Adversarial Networks](https://mlsanity.com/paper/1703.10593/) (2017)
- [FFDNet: Toward a Fast and Flexible Solution for CNN based Image Denoising](https://mlsanity.com/paper/1710.04026/) (2017)
- [Noise2Void - Learning Denoising from Single Noisy Images](https://mlsanity.com/paper/1811.10980/) (2018)
- [Toward Convolutional Blind Denoising of Real Photographs](https://mlsanity.com/paper/1807.04686/) (2018)
- [Unprocessing Images for Learned Raw Denoising](https://mlsanity.com/paper/1811.11127/) (2018)
- [MemNet: A Persistent Memory Network for Image Restoration](https://mlsanity.com/paper/1708.02209/) (2017)
- [CycleISP: Real Image Restoration via Improved Data Synthesis](https://mlsanity.com/paper/2003.07761/) (2020)
- [Benchmarking Denoising Algorithms with Real Photographs](https://mlsanity.com/paper/1707.01313/) (2017)
- [Trainable Nonlinear Reaction Diffusion: A Flexible Framework for Fast and Effective Image Restoration](https://mlsanity.com/paper/1508.02848/) (2015)
- [Residual Dense Network for Image Restoration](https://mlsanity.com/paper/1812.10477/) (2018)
- [Real Image Denoising with Feature Attention](https://mlsanity.com/paper/1904.07396/) (2019)
- [Non-Local Recurrent Network for Image Restoration](https://mlsanity.com/paper/1806.02919/) (2018)
- [A Physics-based Noise Formation Model for Extreme Low-light Raw Denoising](https://mlsanity.com/paper/2003.12751/) (2020)
- [Neural Nearest Neighbors Networks](https://mlsanity.com/paper/1810.12575/) (2018)
- [AP-BSN: Self-Supervised Denoising for Real-World Images via Asymmetric PD and Blind-Spot Network](https://mlsanity.com/paper/2203.11799/) (2022)
- [Transfer Learning from Synthetic to Real-Noise Denoising with Adaptive Instance Normalization](https://mlsanity.com/paper/2002.11244/) (2020)
- [NBNet: Noise Basis Learning for Image Denoising with Subspace Projection](https://mlsanity.com/paper/2012.15028/) (2020)
- [Unpaired Learning of Deep Image Denoising](https://mlsanity.com/paper/2008.13711/) (2020)
- [Invertible Denoising Network: A Light Solution for Real Noise Removal](https://mlsanity.com/paper/2104.10546/) (2021)
- [Spatial-Adaptive Network for Single Image Denoising](https://mlsanity.com/paper/2001.10291/) (2020)
- [C2N: Practical Generative Noise Modeling for Real-World Denoising](https://mlsanity.com/paper/2202.09533/) (2022)
- [When AWGN-based Denoiser Meets Real Noises](https://mlsanity.com/paper/1904.03485/) (2019)
- [CVF-SID: Cyclic multi-Variate Function for Self-Supervised Image Denoising by Disentangling Noise from Image](https://mlsanity.com/paper/2203.13009/) (2022)
- [Learning Invariant Representation for Unsupervised Image Restoration](https://mlsanity.com/paper/2003.12769/) (2020)
- [Unsupervised Single Image Deraining with Self-supervised Constraints](https://mlsanity.com/paper/1811.08575/) (2018)
- [GAN2GAN: Generative Noise Learning for Blind Denoising with Single Noisy Images](https://mlsanity.com/paper/1905.10488/) (2019)
- [Enhancing and Learning Denoiser without Clean Reference](https://mlsanity.com/paper/2009.04286/) (2020)

## Built on by (most cited first, 29 of 29)

- [Degradation-Aware Feature Perturbation for All-in-One Image Restoration](https://mlsanity.com/paper/2505.12630/) (2025)
- [Visual-Instructed Degradation Diffusion for All-in-One Image Restoration](https://mlsanity.com/paper/2506.16960/) (2025)
- [Restore Anything with Masks: Leveraging Mask Image Modeling for Blind All-in-One Image Restoration](https://mlsanity.com/paper/2409.19403/) (2024)
- [Multi-task Image Restoration Guided By Robust DINO Features](https://mlsanity.com/paper/2312.01677/) (2023)
- [Channel Consistency Prior and Self-Reconstruction Strategy Based Unsupervised Image Deraining](https://mlsanity.com/paper/2503.18703/) (2025)
- [Reversing Flow for Image Restoration](https://mlsanity.com/paper/2506.16961/) (2025)
- [Rethinking Transformer-Based Blind-Spot Network for Self-Supervised Image Denoising](https://mlsanity.com/paper/2404.07846/) (2024)
- [Re-boosting Self-Collaboration Parallel Prompt GAN for Unsupervised Image Restoration](https://mlsanity.com/paper/2408.09241/) (2024)
- [Asymmetric Mask Scheme for Self-Supervised Real Image Denoising](https://mlsanity.com/paper/2407.06514/) (2024)
- [Dark-ISP: Enhancing RAW Image Processing for Low-Light Object Detection](https://mlsanity.com/paper/2509.09183/) (2025)
- [SeNM-VAE: Semi-Supervised Noise Modeling with Hierarchical Variational Autoencoder](https://mlsanity.com/paper/2403.17502/) (2024)
- [TPGDiff: Hierarchical Triple-Prior Guided Diffusion for Image Restoration](https://mlsanity.com/paper/2601.20306/) (2026)
- [Masked Pre-training Enables Universal Zero-shot Denoiser](https://mlsanity.com/paper/2401.14966/) (2024)
- [Adaptive Blind All-in-One Image Restoration](https://mlsanity.com/paper/2411.18412/) (2024)
- [Unpaired Image Deraining Using Reward-Guided Self-Reinforcement Strategy](https://mlsanity.com/paper/2605.00719/) (2026)
- [DestripeCycleGAN: Stripe Simulation CycleGAN for Unsupervised Infrared Image Destriping](https://mlsanity.com/paper/2402.09101/) (2024)
- [Dual-Representation Interaction Driven Image Quality Assessment with Restoration Assistance](https://mlsanity.com/paper/2411.17390/) (2024)
- [LUMINA-Net: Low-light Upgrade through Multi-stage Illumination and Noise Adaptation Network for Image Enhancement](https://mlsanity.com/paper/2502.15186/) (2025)
- [Unsupervised Real-World Denoising: Sparsity is All You Need](https://mlsanity.com/paper/2503.21377/) (2025)
- [RAM++: Robust Representation Learning via Adaptive Mask for All-in-One Image Restoration](https://mlsanity.com/paper/2509.12039/) (2025)
- [Circular Quasiconformal Deturbulence: Geometry-Based Restoration from Multiple Turbulent Frames](https://mlsanity.com/paper/2504.13432/) (2025)
- [Zero-Shot CFC: Fast Real-World Image Denoising based on Cross-Frequency Consistency](https://mlsanity.com/paper/2510.12646/) (2025)
- [A Machine Learning-Driven Solution for Denoising Inertial Confinement Fusion Images](https://mlsanity.com/paper/2511.16717/) (2025)
- [SLER-IR: Spherical Layer-wise Expert Routing for All-in-One Image Restoration](https://mlsanity.com/paper/2603.05940/) (2026)
- [QuReC: All-in-One Image Restoration with Query-Specific Guidance and Local-Global Response Calibration](https://mlsanity.com/paper/2607.15097/) (2026)
- [Beyond Uniform Restoration: Empowering All-in-One Restoration with Pixel-Level Multimodal Guidance](https://mlsanity.com/paper/2608.09482/) (2026)
- [DPC-Net: Dual-Prior Collaborative Network for All-in-One Image Restoration](https://mlsanity.com/paper/2608.20141/) (2026)
- [Diffusion Based Unpaired Data Learning for Inverse Problems](https://mlsanity.com/paper/2609.01370/) (2026)
- [Adversarial Training for Pixel Diffusion](https://mlsanity.com/paper/2609.38170/) (2026)

## Abstract (arXiv)

Deep learning methods have shown remarkable performance in image denoising, particularly when trained on large-scale paired datasets. However, acquiring such paired datasets for real-world scenarios poses a significant challenge. Although unsupervised approaches based on generative adversarial networks offer a promising solution for denoising without paired datasets, they are difficult in surpassing the performance limitations of conventional GAN-based unsupervised frameworks without significantly modifying existing structures or increasing the computational complexity of denoisers. To address this problem, we propose a SC strategy for multiple denoisers. This strategy can achieve significant performance improvement without increasing the inference complexity of the GAN-based denoising framework. Its basic idea is to iteratively replace the previous less powerful denoiser in the filter-guided noise extraction module with the current powerful denoiser. This process generates better synthetic clean-noisy image pairs, leading to a more powerful denoiser for the next iteration. This baseline ensures the stability and effectiveness of the training network. The experimental results demonstrate the superiority of our method over state-of-the-art unsupervised methods.