# Multi-Scale Adaptive Network for Single Image Denoising

arXiv: https://arxiv.org/abs/2203.04313 · HTML: https://mlsanity.com/paper/2203.04313/

## Key facts

- Cited by 17 papers in the map (data through 14 Oct 2026).
- Builds on 37 of its 76 references that are in the map.
- First posted on arXiv 8 Mar 2022.

## Builds on (most cited first, 30 of 37)

- [Deep Residual Learning for Image Recognition](https://mlsanity.com/paper/1512.03385/) (2015)
- [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)
- [SGDR: Stochastic Gradient Descent with Warm Restarts](https://mlsanity.com/paper/1608.03983/) (2016)
- [Non-local Neural Networks](https://mlsanity.com/paper/1711.07971/) (2017)
- [SwinIR: Image Restoration Using Swin Transformer](https://mlsanity.com/paper/2108.10257/) (2021)
- [Restormer: Efficient Transformer for High-Resolution Image Restoration](https://mlsanity.com/paper/2111.09881/) (2021)
- [Beyond a Gaussian Denoiser: Residual Learning of Deep CNN for Image Denoising](https://mlsanity.com/paper/1608.03981/) (2016)
- [Deep High-Resolution Representation Learning for Human Pose Estimation](https://mlsanity.com/paper/1902.09212/) (2019)
- [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)
- [Deformable ConvNets v2: More Deformable, Better Results](https://mlsanity.com/paper/1811.11168/) (2018)
- [FFDNet: Toward a Fast and Flexible Solution for CNN based Image Denoising](https://mlsanity.com/paper/1710.04026/) (2017)
- [Toward Convolutional Blind Denoising of Real Photographs](https://mlsanity.com/paper/1807.04686/) (2018)
- [Density-aware Single Image De-raining using a Multi-stream Dense Network](https://mlsanity.com/paper/1802.07412/) (2018)
- [MemNet: A Persistent Memory Network for Image Restoration](https://mlsanity.com/paper/1708.02209/) (2017)
- [Residual Non-local Attention Networks for Image Restoration](https://mlsanity.com/paper/1903.10082/) (2019)
- [Multi-Scale Boosted Dehazing Network with Dense Feature Fusion](https://mlsanity.com/paper/2004.13388/) (2020)
- [GridDehazeNet: Attention-Based Multi-Scale Network for Image Dehazing](https://mlsanity.com/paper/1908.03245/) (2019)
- [Benchmarking Denoising Algorithms with Real Photographs](https://mlsanity.com/paper/1707.01313/) (2017)
- [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)
- [Receptive Field Block Net for Accurate and Fast Object Detection](https://mlsanity.com/paper/1711.07767/) (2017)
- [Neural Nearest Neighbors Networks](https://mlsanity.com/paper/1810.12575/) (2018)
- [Real-world Noisy Image Denoising: A New Benchmark](https://mlsanity.com/paper/1804.02603/) (2018)
- [Spatial-Adaptive Network for Single Image Denoising](https://mlsanity.com/paper/2001.10291/) (2020)
- [You Only Look Yourself: Unsupervised and Untrained Single Image Dehazing Neural Network](https://mlsanity.com/paper/2006.16829/) (2020)
- [When AWGN-based Denoiser Meets Real Noises](https://mlsanity.com/paper/1904.03485/) (2019)
- [A Trilateral Weighted Sparse Coding Scheme for Real-World Image Denoising](https://mlsanity.com/paper/1807.04364/) (2018)
- [Path-Restore: Learning Network Path Selection for Image Restoration](https://mlsanity.com/paper/1904.10343/) (2019)

## Built on by (most cited first, 17 of 17)

- [Adaptive Dynamic Filtering Network for Image Denoising](https://mlsanity.com/paper/2211.12051/) (2022)
- [Test-Time Degradation Adaptation for Open-Set Image Restoration](https://mlsanity.com/paper/2312.02197/) (2023)
- [EWT: Efficient Wavelet-Transformer for Single Image Denoising](https://mlsanity.com/paper/2304.06274/) (2023)
- [Learning Multi-scale Spatial-frequency Features for Image Denoising](https://mlsanity.com/paper/2506.16307/) (2025)
- [Beyond Image Prior: Embedding Noise Prior into Conditional Denoising Transformer](https://mlsanity.com/paper/2407.09094/) (2024)
- [Exploration of Lightweight Single Image Denoising with Transformers and Truly Fair Training](https://mlsanity.com/paper/2304.01805/) (2023)
- [Joint End-to-End Image Compression and Denoising: Leveraging Contrastive Learning and Multi-Scale Self-ONNs](https://mlsanity.com/paper/2402.05582/) (2024)
- [Prompting Lipschitz-constrained network for multiple-in-one sparse-view CT reconstruction](https://mlsanity.com/paper/2511.20296/) (2025)
- [All-in-One Video Restoration under Smoothly Evolving Unknown Weather Degradations](https://mlsanity.com/paper/2601.00533/) (2026)
- [Spiking Pyramid Wavelet Transformation for High-efficient and Low-energy Image Restoration](https://mlsanity.com/paper/2606.18644/) (2026)
- [Next-Scale Prediction: A Self-Supervised Approach for Real-World Image Denoising](https://mlsanity.com/paper/2512.21038/) (2025)
- [PixIE: Prompted Pixel-Space Low-Light Image Enhancement](https://mlsanity.com/paper/2605.23531/) (2026)
- [Look-Around Before You Leap: High-Frequency Injected Transformer for Image Restoration](https://mlsanity.com/paper/2404.00279/) (2024)
- [Combining Pre- and Post-Demosaicking Noise Removal for RAW Video](https://mlsanity.com/paper/2410.02572/) (2024)
- [Prompt Guiding Multi-Scale Adaptive Sparse Representation-driven Network for Low-Dose CT MAR](https://mlsanity.com/paper/2504.19687/) (2025)
- [QWD-GAN: Quality-aware Wavelet-driven GAN for Unsupervised Medical Microscopy Images Denoising](https://mlsanity.com/paper/2509.15814/) (2025)
- [Learned Nonlocal Feature Matching and Filtering for RAW Image Denoising](https://mlsanity.com/paper/2604.17453/) (2026)

## Abstract (arXiv)

Multi-scale architectures have shown effectiveness in a variety of tasks thanks to appealing cross-scale complementarity. However, existing architectures treat different scale features equally without considering the scale-specific characteristics, textiti.e., the within-scale characteristics are ignored in the architecture design. In this paper, we reveal this missing piece for multi-scale architecture design and accordingly propose a novel Multi-Scale Adaptive Network (MSANet) for single image denoising. Specifically, MSANet simultaneously embraces the within-scale characteristics and the cross-scale complementarity thanks to three novel neural blocks, textiti.e., adaptive feature block (AFeB), adaptive multi-scale block (AMB), and adaptive fusion block (AFuB). In brief, AFeB is designed to adaptively preserve image details and filter noises, which is highly expected for the features with mixed details and noises. AMB could enlarge the receptive field and aggregate the multi-scale information, which meets the need of contextually informative features. AFuB devotes to adaptively sampling and transferring the features from one scale to another scale, which fuses the multi-scale features with varying characteristics from coarse to fine. Extensive experiments on both three real and six synthetic noisy image datasets show the superiority of MSANet compared with 12 methods. The code could be accessed from https://github.com/XLearning-SCU/2022-NeurIPS-MSANet.