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Real Image Denoising with Feature Attention

arXiv 1904.07396 ↗First posted 16 Apr 2019cs.CV cs.LGcited by 176

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Abstract (arXiv)

Deep convolutional neural networks perform better on images containing spatially invariant noise (synthetic noise); however, their performance is limited on real-noisy photographs and requires multiple stage network modeling. To advance the practicability of denoising algorithms, this paper proposes a novel single-stage blind real image denoising network (RIDNet) by employing a modular architecture. We use a residual on the residual structure to ease the flow of low-frequency information and apply feature attention to exploit the channel dependencies. Furthermore, the evaluation in terms of quantitative metrics and visual quality on three synthetic and four real noisy datasets against 19 state-of-the-art algorithms demonstrate the superiority of our RIDNet.

Key facts. Cited by 176 papers in the map (data through 7 Oct 2026). First posted on arXiv 16 Apr 2019.

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Citing papers per year: 2020: 1; 2021: 32; 2022: 44; 2023: 41; 2024: 32; 2025: 18; 2026: 8.

176 papers.

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  1. 2021Restormer: Efficient Transformer for High-Resolution Image Restorationcited by 1,618
  2. 2021Uformer: A General U-Shaped Transformer for Image Restorationcited by 864
  3. 2021Multi-Stage Progressive Image Restorationcited by 813
  4. 2022MAXIM: Multi-Axis MLP for Image Processingcited by 301
  5. 2021HINet: Half Instance Normalization Network for Image Restorationcited by 236
  6. 2022Learning Enriched Features for Fast Image Restoration and Enhancementcited by 150
  7. 2022Deep Generalized Unfolding Networks for Image Restorationcited by 119
  8. 2021Data Collection and Quality Challenges in Deep Learning: A Data-Centric AI Perspectivecited by 116
  9. 2022Blind2Unblind: Self-Supervised Image Denoising with Visible Blind Spotscited by 83
  10. 2023Image Denoising: The Deep Learning Revolution and Beyond -- A Survey Paper --cited by 71
  11. 2021Invertible Denoising Network: A Light Solution for Real Noise Removalcited by 68
  12. 2021Physics-based Noise Modeling for Extreme Low-light Photographycited by 67
38 more
  1. 2025HVI: A New Color Space for Low-light Image Enhancementcited by 62
  2. 2021Improving Image Restoration by Revisiting Global Information Aggregationcited by 60
  3. 2023Masked Image Training for Generalizable Deep Image Denoisingcited by 57
  4. 2022C2N: Practical Generative Noise Modeling for Real-World Denoisingcited by 56
  5. 2021Dynamic Attentive Graph Learning for Image Restorationcited by 48
  6. 2022CVF-SID: Cyclic multi-Variate Function for Self-Supervised Image Denoising by Disentangling Noise from Imagecited by 45
  7. 2022Visual Attention Methods in Deep Learning: An In-Depth Surveycited by 37
  8. 2023Xformer: Hybrid X-Shaped Transformer for Image Denoisingcited by 37
  9. 2023LG-BPN: Local and Global Blind-Patch Network for Self-Supervised Real-World Denoisingcited by 37
  10. 2023Improving Image Restoration through Removing Degradations in Textual Representationscited by 36
  11. 2023KBNet: Kernel Basis Network for Image Restorationcited by 33
  12. 2023Spatially Adaptive Self-Supervised Learning for Real-World Image Denoisingcited by 33
  13. 2023Random Sub-Samples Generation for Self-Supervised Real Image Denoisingcited by 31
  14. 2023MM-BSN: Self-Supervised Image Denoising for Real-World with Multi-Mask based on Blind-Spot Networkcited by 30
  15. 2022Learning to Generate Realistic Noisy Images via Pixel-level Noise-aware Adversarial Trainingcited by 29
  16. 2023Unsupervised Image Denoising in Real-World Scenarios via Self-Collaboration Parallel Generative Adversarial Branchescited by 29
  17. 2022Adaptive Dynamic Filtering Network for Image Denoisingcited by 27
  18. 2023Diffusion Model for Generative Image Denoisingcited by 27
  19. 2025Visual-Instructed Degradation Diffusion for All-in-One Image Restorationcited by 26
  20. 2022Restricted Black-box Adversarial Attack Against DeepFake Face Swappingcited by 25
  21. 2022Variational Deep Image Restorationcited by 24
  22. 2024CU-Mamba: Selective State Space Models with Channel Learning for Image Restorationcited by 24
  23. 2021Joint Denoising and Demosaicking with Green Channel Prior for Real-world Burst Imagescited by 18
  24. 2022Learn from Unpaired Data for Image Restoration: A Variational Bayes Approachcited by 18
  25. 2022Perceptual Image Enhancement for Smartphone Real-Time Applicationscited by 18
  26. 2022Multi-Scale Adaptive Network for Single Image Denoisingcited by 17
  27. 2023Learning Distortion Invariant Representation for Image Restoration from A Causality Perspectivecited by 17
  28. 2023Exploring Efficient Asymmetric Blind-Spots for Self-Supervised Denoising in Real-World Scenarioscited by 17
  29. 2023PottsMGNet: A Mathematical Explanation of Encoder-Decoder Based Neural Networkscited by 17
  30. 2023Unleashing the Power of Self-Supervised Image Denoising: A Comprehensive Reviewcited by 17
  31. 2021COLA-Net: Collaborative Attention Network for Image Restorationcited by 16
  32. 2023HQ-50K: A Large-scale, High-quality Dataset for Image Restorationcited by 15
  33. 2022D2HNet: Joint Denoising and Deblurring with Hierarchical Network for Robust Night Image Restorationcited by 14
  34. 2022Fed-FSNet: Mitigating Non-I.I.D. Federated Learning via Fuzzy Synthesizing Networkcited by 14
  35. 2023Mixed Hierarchy Network for Image Restorationcited by 14
  36. 2022Towards Adversarially Robust Deep Image Denoisingcited by 13
  37. 2023Self-Supervised Image Denoising for Real-World Images with Context-aware Transformercited by 13
  38. 2022Rethinking Performance Gains in Image Dehazing Networkscited by 12

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