mlsanityData to 7 Oct 2026

Selective Residual M-Net for Real Image Denoising

arXiv 2203.01645 ↗First posted 3 Mar 2022eess.IV cs.AI cs.CVcited by 5

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Abstract (arXiv)

Image restoration is a low-level vision task which is to restore degraded images to noise-free images. With the success of deep neural networks, the convolutional neural networks surpass the traditional restoration methods and become the mainstream in the computer vision area. To advance the performanceof denoising algorithms, we propose a blind real image denoising network (SRMNet) by employing a hierarchical architecture improved from U-Net. Specifically, we use a selective kernel with residual block on the hierarchical structure called M-Net to enrich the multi-scale semantic information. Furthermore, our SRMNet has competitive performance results on two synthetic and two real-world noisy datasets in terms of quantitative metrics and visual quality. The source code and pretrained model are available at https://github.com/TentativeGitHub/SRMNet.

Key facts. Cited by 5 papers in the map (data through 7 Oct 2026). Builds on 24 of its 57 references that are in the map. First posted on arXiv 3 Mar 2022.

Builds on

24 of 57 references.

References · 24

  1. 2015Deep Residual Learning for Image Recognitioncited by 51,809
  2. 2015U-Net: Convolutional Networks for Biomedical Image Segmentationcited by 19,604
  3. 2016Densely Connected Convolutional Networkscited by 6,691
  4. 2015SegNet: A Deep Convolutional Encoder-Decoder Architecture for Image Segmentationcited by 1,483
  5. 2016Beyond a Gaussian Denoiser: Residual Learning of Deep CNN for Image Denoisingcited by 1,352
  6. 2018Image Super-Resolution Using Very Deep Residual Channel Attention Networkscited by 1,256
  7. 2016Real-Time Single Image and Video Super-Resolution Using an Efficient Sub-Pixel Convolutional Neural Networkcited by 1,199
  8. 2021Multi-Stage Progressive Image Restorationcited by 813
  9. 2018Residual Dense Network for Image Super-Resolutioncited by 796
  10. 2017FFDNet: Toward a Fast and Flexible Solution for CNN based Image Denoisingcited by 509
  11. 2017Learning Deep CNN Denoiser Prior for Image Restorationcited by 407
  12. 2019Selective Kernel Networkscited by 401
12 more
  1. 2018Toward Convolutional Blind Denoising of Real Photographscited by 274
  2. 2019Residual Non-local Attention Networks for Image Restorationcited by 236
  3. 2020CycleISP: Real Image Restoration via Improved Data Synthesiscited by 212
  4. 2017Benchmarking Denoising Algorithms with Real Photographscited by 209
  5. 2019Real Image Denoising with Feature Attentioncited by 176
  6. 2017CondenseNet: An Efficient DenseNet using Learned Group Convolutionscited by 121
  7. 2019Variational Denoising Network: Toward Blind Noise Modeling and Removalcited by 117
  8. 2020Dual Adversarial Network: Toward Real-world Noise Removal and Noise Generationcited by 101
  9. 2020Transfer Learning from Synthetic to Real-Noise Denoising with Adaptive Instance Normalizationcited by 92
  10. 2020Spatial-Adaptive Network for Single Image Denoisingcited by 64
  11. 2021Dynamic Attentive Graph Learning for Image Restorationcited by 48
  12. 2018FPD-M-net: Fingerprint Image Denoising and Inpainting Using M-Net Based Convolutional Neural Networkscited by 5

Built on by

Cited by year

Citing papers per year: 2023: 2; 2024: 2; 2025: 1.

5 papers.

  1. 2024CascadedGaze: Efficiency in Global Context Extraction for Image Restorationcited by 16
  2. 2023DDT: Dual-branch Deformable Transformer for Image Denoisingcited by 3
  3. 2025OBIFormer: A Fast Attentive Denoising Framework for Oracle Bone Inscriptionscited by 2
  4. 2023Noise-to-Norm Reconstruction for Industrial Anomaly Detection and Localizationcited by 1
  5. 2024GAMA-IR: Global Additive Multidimensional Averaging for Fast Image Restorationcited by 0