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Top AI Repos tracks AI repositories on GitHub and answers two different questions about each one: is it moving right now, and would you bet a product on it.
Top AI Repos tracks AI repositories on GitHub and answers two different questions about each one: is it moving right now, and would you bet a product on it.
One-paper-one-short-contribution-summary of all latest image/burst/video Denoising papers with code & citation published in top conference and journal.
| Date | Stars |
|---|---|
| 2026-07-30 | 503 |
| 2026-08-07 | 504 |
| 2026-09-24 | 504 |
| 2026-10-20 | 505 |
| 2026-10-27 | 506 |
| 2026-11-01 | 505 |
| 2026-11-04 | 505 |
Today
— stars today
This week
-1 stars this week
This month
+1 stars this month
Momentum
0.0
growth rate 0.00%/day
# Awesome-Denoise
There are three main factors to divide these papers into different catrgories to have a better idea.
Sometimes raw domain denoising papers would use some ISP to convert to sRGB domain, So use Both to cover this situation.
Sometimes video denoising papers degrade to burst denoising, even single image denoising, always use Video tag to cover this situation.
* Color Space
* RGB
* Raw
* Both
* Image Kind
* Single
* Burst
* Video
* Noise Model
* AWGN(Additive White Gaussian Noise model)
* PG(Posion Gaussian noise model)
* GAN(Gan based noise model)
* Real(camera or dlsr devices real noise model)
* Prior
* Low Rank
* Sparsity
* self similarity
## benchmark dataset
* SIDD, CVPR 2018, citation 256
* [A High-Quality Denoising Dataset for Smartphone Cameras](https://openaccess.thecvf.com/content_cvpr_2018/papers/Abdelhamed_A_High-Quality_Denoising_CVPR_2018_paper.pdf)
* [Matlab](https://github.com/AbdoKamel/sidd-ground-truth-image-estimation)
* RENOIR, JVCIR 2018, citation 106
* [RENOIR–A dataset for real low-light image noise reduction](https://arxiv.org/pdf/1409.8230.pdf)
* [broken dataset link](http://adrianbarburesearch.blogspot.com/p/renoir-dataset.html)
* PolyU, arxiv 2018, citation 108
* [Real-world Noisy Image Denoising: A New Benchmark](https://arxiv.org/pdf/1804.02603.pdf)
* [Matlab](https://github.com/csjunxu/PolyU-Real-World-Noisy-Images-Dataset)
* SID, CVPR 2018, citation 595
* [Learning to see in the dark](https://openaccess.thecvf.com/content_cvpr_2018/papers/Chen_Learning_to_See_CVPR_2018_paper.pdf)
* [Tensorflow](https://github.com/cchen156/Learning-to-See-in-the-Dark)
* DND, CVPR 2017, citation 296
* [Benchmarking Denoising Algorithms with Real Photographs](https://openaccess.thecvf.com/content_cvpr_2017/papers/Plotz_Benchmarking_Denoising_Algorithms_CVPR_2017_paper.pdf)
* [homepage](https://noise.visinf.tu-darmstadt.de/)
* NaM, CVPR 2016, citation 148
* [A Holistic Approach to Cross-Channel Image Noise Modeling and its Application to Image Denoising](https://openaccess.thecvf.com/content_cvpr_2016/papers/Nam_A_Holistic_Approach_CVPR_2016_paper.pdf)|
# self-supervised denoising
video denoising
+ [Unsupervised deep video denoising](http://openaccess.thecvf.com/content/ICCV2021/html/Sheth_Unsupervised_Deep_Video_Denoising_ICCV_2021_paper.html)
+ ICCV 2021, UDVD
+ [Recurrent Self-Supervised Video Denoising with Denser
Receptive Field](https://arxiv.org/pdf/2308.03608.pdf)
+ ACM MM 2023, [code](https://github.com/Wang-XIaoDingdd/RDRF)
image denoising
|Index|Year|Pub|Title|cite|
|:---:|:---:|:---:|:---:|:---:|
|1|2018|ICML|[Noise2Noise: Learning image restoration without clean data](https://arxiv.org/pdf/1803.04189.pdf)|1236|
|2|2019|CVPR|[Noise2void-learning denoising from single noisy images](http://openaccess.thecvf.com/content_CVPR_2019/html/Krull_Noise2Void_-_Learning_Denoising_From_Single_Noisy_Images_CVPR_2019_paper.html)|748|
|3|2019|ICML|[Noise2self: Blind denoising by self-supervision](http://proceedings.mlr.press/v97/batson19a.html)|441|
|4|2019|NeurIPS|[High-quality self-supervised deep image denoising](https://proceedings.neurips.cc/paper/8920-high-quality-self-supervised-deep-image-denoising)|247|
|5|2019|arxiv|[Unsupervised image noise modeling with self-consistent GAN](https://arxiv.org/pdf/1906.05762.pdf)|13|
|6|2020|Frontiers in Computer Science|[Probabilistic noise2void: Unsupervised content-aware denoising](https://www.frontiersin.org/articles/10.3389/fcomp.2020.00005/full)|119|
|7|2020|TIP|[Noisy-as-clean: Learning self-supervised denoising from corrupted image](https://arxiv.org/pdf/1906.06878.pdf)|112|
|8|2020|CVPR|[Self2self with dropout: Learning self-supervised denoising from single image](http://openaccess.thecvf.com/content_CVPR_2020/html/Quan_Self2Self_With_Dropout_Learning_Self-Supervised_Denoising_From_Single_Image_CVPR_2020_paper.html)|201|
|9|2020|CVPR|[Noisier2noise: Learning to denoiExcerpt of 32,905 characters
Read on GitHubWould you bet a product on this? Bounded 0–100 and slow moving.
matched fp:ec8b3388c0de272d, topic:deep-learning, topic:pytorch, topic:tensorflow
matched fp:ec8b3388c0de272d, topic:awesome, topic:papers
