mlsanityData to 7 Oct 2026

RENOIR - A Dataset for Real Low-Light Image Noise Reduction

arXiv 1409.8230 ↗First posted 29 Sep 2014cs.CVcited by 36

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Abstract (arXiv)

Image denoising algorithms are evaluated using images corrupted by artificial noise, which may lead to incorrect conclusions about their performances on real noise. In this paper we introduce a dataset of color images corrupted by natural noise due to low-light conditions, together with spatially and intensity-aligned low noise images of the same scenes. We also introduce a method for estimating the true noise level in our images, since even the low noise images contain small amounts of noise. We evaluate the accuracy of our noise estimation method on real and artificial noise, and investigate the Poisson-Gaussian noise model. Finally, we use our dataset to evaluate six denoising algorithms: Active Random Field, BM3D, Bilevel-MRF, Multi-Layer Perceptron, and two versions of NL-means. We show that while the Multi-Layer Perceptron, Bilevel-MRF, and NL-means with soft threshold outperform BM3D on gray images with synthetic noise, they lag behind on our dataset.

Key facts. Cited by 36 papers in the map (data through 7 Oct 2026). First posted on arXiv 29 Sep 2014.

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Citing papers per year: 2021: 4; 2022: 9; 2023: 11; 2024: 4; 2025: 4; 2026: 4.

36 papers.

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  1. 2022Multitask AET with Orthogonal Tangent Regularity for Dark Object Detectioncited by 59
  2. 2022C2N: Practical Generative Noise Modeling for Real-World Denoisingcited by 56
  3. 2022Dancing under the stars: video denoising in starlightcited by 47
  4. 2023Instance Segmentation in the Darkcited by 45
  5. 2022Learn from Unpaired Data for Image Restoration: A Variational Bayes Approachcited by 18
  6. 2022Multi-Scale Adaptive Network for Single Image Denoisingcited by 17
  7. 2022Noise2NoiseFlow: Realistic Camera Noise Modeling without Clean Imagescited by 17
  8. 2023Unleashing the Power of Self-Supervised Image Denoising: A Comprehensive Reviewcited by 17
  9. 2024LAN: Learning to Adapt Noise for Image Denoisingcited by 10
  10. 2023RViDeformer: Efficient Raw Video Denoising Transformer with a Larger Benchmark Datasetcited by 8
  11. 2023SVNR: Spatially-variant Noise Removal with Denoising Diffusioncited by 8
  12. 2023A Comparison of Image Denoising Methodscited by 7
24 more
  1. 2025Rotation-Equivariant Self-Supervised Method in Image Denoisingcited by 6
  2. 2023DarkVision: A Benchmark for Low-light Image/Video Perceptioncited by 5
  3. 2024PIE: Physics-inspired Low-light Enhancementcited by 5
  4. 2026Beyond Model Design: Data-Centric Training and Self-Ensemble for Gaussian Color Image Denoisingcited by 5
  5. 2022Enhancing Low-Light Images in Real World via Cross-Image Disentanglementcited by 4
  6. 2023NoiseTransfer: Image Noise Generation with Contrastive Embeddingscited by 4
  7. 2023Image Blind Denoising Using Dual Convolutional Neural Network with Skip Connectioncited by 4
  8. 2024Latent Neural Cellular Automata for Resource-Efficient Image Restorationcited by 4
  9. 2025Dark Noise Diffusion: Noise Synthesis for Low-Light Image Denoisingcited by 4
  10. 2022Going Deeper into Recognizing Actions in Dark Environments: A Comprehensive Benchmark Studycited by 3
  11. 2023DCANet: Dual Convolutional Neural Network with Attention for Image Blind Denoisingcited by 3
  12. 2024Beyond Image Prior: Embedding Noise Prior into Conditional Denoising Transformercited by 3
  13. 2021Feature-Align Network with Knowledge Distillation for Efficient Denoisingcited by 2
  14. 2021Convolutional versus Self-Organized Operational Neural Networks for Real-World Blind Image Denoisingcited by 2
  15. 2021CERL: A Unified Optimization Framework for Light Enhancement with Realistic Noisecited by 2
  16. 20252-Shots in the Dark: Low-Light Denoising with Minimal Data Acquisitioncited by 2
  17. 2026How far have we gone in Generative Image Restoration? A study on its capability, limitations and evaluation practicescited by 2
  18. 2021Helsinki Deblur Challenge 2021: description of photographic datacited by 1
  19. 2023Hierarchical Disentangled Representation for Invertible Image Denoising and Beyondcited by 1
  20. 2022Towards Real-World Video Denosing: A Practical Video Denosing Dataset and Networkcited by 0
  21. 2023Unsupervised Low Light Image Enhancement Using SNR-Aware Swin Transformercited by 0
  22. 2025Net2Net: When Un-trained Meets Pre-trained Networks for Robust Real-World Denoisingcited by 0
  23. 2026DRIFT: Deep Restoration, ISP Fusion, and Tone-mappingcited by 0
  24. 2026sRGB Real Noise Modeling via Noise-Aware Sampling with Normalizing Flowscited by 0