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NTIRE 2020 Challenge on Real Image Denoising: Dataset, Methods and Results

arXiv 2005.04117 ↗First posted 8 May 2020cs.CV eess.IVcited by 51

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Abstract (arXiv)

This paper reviews the NTIRE 2020 challenge on real image denoising with focus on the newly introduced dataset, the proposed methods and their results. The challenge is a new version of the previous NTIRE 2019 challenge on real image denoising that was based on the SIDD benchmark. This challenge is based on a newly collected validation and testing image datasets, and hence, named SIDD+. This challenge has two tracks for quantitatively evaluating image denoising performance in (1) the Bayer-pattern rawRGB and (2) the standard RGB (sRGB) color spaces. Each track ~250 registered participants. A total of 22 teams, proposing 24 methods, competed in the final phase of the challenge. The proposed methods by the participating teams represent the current state-of-the-art performance in image denoising targeting real noisy images. The newly collected SIDD+ datasets are publicly available at: https://bit.ly/siddplus_data.

Key facts. Cited by 51 papers in the map (data through 7 Oct 2026). First posted on arXiv 8 May 2020.

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Citing papers per year: 2021: 12; 2022: 13; 2023: 5; 2024: 10; 2025: 5; 2026: 6.

51 papers.

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  1. 2021Early Stopping for Deep Image Priorcited by 44
  2. 2022Model-Based Image Signal Processors via Learnable Dictionariescited by 43
  3. 2021IDR: Self-Supervised Image Denoising via Iterative Data Refinementcited by 36
  4. 2021Real-Time Quantized Image Super-Resolution on Mobile NPUs, Mobile AI 2021 Challenge: Reportcited by 34
  5. 2022Atmospheric Turbulence Removal with Complex-Valued Convolutional Neural Networkcited by 21
  6. 2021Fast and Accurate Single-Image Depth Estimation on Mobile Devices, Mobile AI 2021 Challenge: Reportcited by 19
  7. 2021Real-Time Video Super-Resolution on Smartphones with Deep Learning, Mobile AI 2021 Challenge: Reportcited by 19
  8. 2022Real-RawVSR: Real-World Raw Video Super-Resolution with a Benchmark Datasetcited by 19
  9. 2022Perceptual Image Enhancement for Smartphone Real-Time Applicationscited by 18
  10. 2022Efficient Visual Computing with Camera RAW Snapshotscited by 18
  11. 2021Progressive Joint Low-light Enhancement and Noise Removal for Raw Imagescited by 16
  12. 2022Efficient and Accurate Quantized Image Super-Resolution on Mobile NPUs, Mobile AI & AIM 2022 challenge: Reportcited by 16
38 more
  1. 2024CascadedGaze: Efficiency in Global Context Extraction for Image Restorationcited by 16
  2. 2021Fast Camera Image Denoising on Mobile GPUs with Deep Learning, Mobile AI 2021 Challenge: Reportcited by 15
  3. 2025NTIRE 2025 Challenge on Efficient Burst HDR and Restoration: Datasets, Methods, and Resultscited by 14
  4. 2022MicroISP: Processing 32MP Photos on Mobile Devices with Deep Learningcited by 13
  5. 2025IDF: Iterative Dynamic Filtering Networks for Generalizable Image Denoisingcited by 12
  6. 2024DualDn: Dual-domain Denoising via Differentiable ISPcited by 11
  7. 2021Pseudo-ISP: Learning Pseudo In-camera Signal Processing Pipeline from A Color Image Denoisercited by 10
  8. 2021Fast and Accurate Quantized Camera Scene Detection on Smartphones, Mobile AI 2021 Challenge: Reportcited by 10
  9. 2024LAN: Learning to Adapt Noise for Image Denoisingcited by 10
  10. 2021Wavelet-Based Network For High Dynamic Range Imagingcited by 9
  11. 2022MIPI 2022 Challenge on Quad-Bayer Re-mosaic: Dataset and Reportcited by 9
  12. 2023RViDeformer: Efficient Raw Video Denoising Transformer with a Larger Benchmark Datasetcited by 8
  13. 2022PyNet-V2 Mobile: Efficient On-Device Photo Processing With Neural Networkscited by 7
  14. 2024CRNet: A Detail-Preserving Network for Unified Image Restoration and Enhancement Taskcited by 6
  15. 2024Complementary Advantages: Exploiting Cross-Field Frequency Correlation for NIR-Assisted Image Denoisingcited by 5
  16. 2026Beyond Model Design: Data-Centric Training and Self-Ensemble for Gaussian Color Image Denoisingcited by 5
  17. 2022Efficient Single-Image Depth Estimation on Mobile Devices, Mobile AI & AIM 2022 Challenge: Reportcited by 4
  18. 2023NoiseTransfer: Image Noise Generation with Contrastive Embeddingscited by 4
  19. 2023Steered Mixture of Experts Regression for Image Denoising with Multi-Model-Inferencecited by 4
  20. 2024NIR-Assisted Image Denoising: A Selective Fusion Approach and A Real-World Benchmark Datasetcited by 4
  21. 2024NTIRE 2024 Challenge on Night Photography Renderingcited by 4
  22. 2024HazeSpace2M: A Dataset for Haze Aware Single Image Dehazingcited by 4
  23. 2021MANAS: Multi-Scale and Multi-Level Neural Architecture Search for Low-Dose CT Denoisingcited by 3
  24. 2022Power Efficient Video Super-Resolution on Mobile NPUs with Deep Learning, Mobile AI & AIM 2022 challenge: Reportcited by 3
  25. 2024Unsupervised Training of a Dynamic Context-Aware Deep Denoising Framework for Low-Dose Fluoroscopic Imagingcited by 2
  26. 2025Vision Language Models Cannot Plan, but Can They Formalize?cited by 2
  27. 2026Real Image Denoising with Knowledge Distillation for High-Performance Mobile NPUscited by 2
  28. 2022Rendering Nighttime Image Via Cascaded Color and Brightness Compensationcited by 1
  29. 2023Cross-domain Denoising for Low-dose Multi-frame Spiral Computed Tomographycited by 1
  30. 2024EnsIR: An Ensemble Algorithm for Image Restoration via Gaussian Mixture Modelscited by 1
  31. 2025T2T-VICL: Cross-Task Visual In-Context Learning via Implicit Text-Driven VLMscited by 1
  32. 2026Diffusion-Based sRGB Real Noise Generation via Prompt-Driven Noise Representation Learningcited by 1
  33. 2021A Sub-band Approach to Deep Denoising Wavelet Networks and a Frequency-adaptive Loss for Perceptual Qualitycited by 0
  34. 2022Lightweight HDR Camera ISP for Robust Perception in Dynamic Illumination Conditions via Fourier Adversarial Networkscited by 0
  35. 2023NM-FlowGAN: Modeling sRGB Noise without Paired Images using a Hybrid Approach of Normalizing Flows and GANcited by 0
  36. 2025A Data-Centric Perspective on the Influence of Image Data Quality in Machine Learning Modelscited by 0
  37. 2026Unpaired Joint Distribution Modeling via Multi-Scale Image Representationscited by 0
  38. 2026YeTI: You Only Need Two Noisy Images for Real-World sRGB Noise Generationcited by 0

50 most cited of 51 All 51 (JSON)