# Natural Image Noise Dataset

arXiv: https://arxiv.org/abs/1906.00270 · HTML: https://mlsanity.com/paper/1906.00270/

## Key facts

- Cited by 20 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 1 Jun 2019.

## Built on by (most cited first, 20 of 20)

- [AP-BSN: Self-Supervised Denoising for Real-World Images via Asymmetric PD and Blind-Spot Network](https://mlsanity.com/paper/2203.11799/) (2022)
- [LG-BPN: Local and Global Blind-Patch Network for Self-Supervised Real-World Denoising](https://mlsanity.com/paper/2304.00534/) (2023)
- [Exploring Efficient Asymmetric Blind-Spots for Self-Supervised Denoising in Real-World Scenarios](https://mlsanity.com/paper/2303.16783/) (2023)
- [Unleashing the Power of Self-Supervised Image Denoising: A Comprehensive Review](https://mlsanity.com/paper/2308.00247/) (2023)
- [PUCA: Patch-Unshuffle and Channel Attention for Enhanced Self-Supervised Image Denoising](https://mlsanity.com/paper/2310.10088/) (2023)
- [Toward Interactive Modulation for Photo-Realistic Image Restoration](https://mlsanity.com/paper/2105.03085/) (2021)
- [Semantic Scene Completion with Cleaner Self](https://mlsanity.com/paper/2303.09977/) (2023)
- [Self-supervised Image Denoising with Downsampled Invariance Loss and Conditional Blind-Spot Network](https://mlsanity.com/paper/2304.09507/) (2023)
- [A Comparison of Image Denoising Methods](https://mlsanity.com/paper/2304.08990/) (2023)
- [Asymmetric Mask Scheme for Self-Supervised Real Image Denoising](https://mlsanity.com/paper/2407.06514/) (2024)
- [WISE: Whitebox Image Stylization by Example-based Learning](https://mlsanity.com/paper/2207.14606/) (2022)
- [On the Importance of Denoising when Learning to Compress Images](https://mlsanity.com/paper/2307.06233/) (2023)
- [SeNM-VAE: Semi-Supervised Noise Modeling with Hierarchical Variational Autoencoder](https://mlsanity.com/paper/2403.17502/) (2024)
- [Learning to Translate Noise for Robust Image Denoising](https://mlsanity.com/paper/2412.04727/) (2024)
- [Benchmarking Deep Learning-Based Object Detection Models on Feature Deficient Astrophotography Imagery Dataset](https://mlsanity.com/paper/2508.06537/) (2025)
- [I2V: Towards Texture-Aware Self-Supervised Blind Denoising using Self-Residual Learning for Real-World Images](https://mlsanity.com/paper/2302.10523/) (2023)
- [NM-FlowGAN: Modeling sRGB Noise without Paired Images using a Hybrid Approach of Normalizing Flows and GAN](https://mlsanity.com/paper/2312.10112/) (2023)
- [Learning Joint Denoising, Demosaicing, and Compression from the Raw Natural Image Noise Dataset](https://mlsanity.com/paper/2501.08924/) (2025)
- [MAD: Manifold Attracted Diffusion](https://mlsanity.com/paper/2509.24710/) (2025)
- [GALOSH: Blind, Training-Free Denoising of Raw Bayer and sRGB Images by Parallel-Friendly Local Shrinkage](https://mlsanity.com/paper/2607.03768/) (2026)

## Abstract (arXiv)

Convolutional neural networks have been the focus of research aiming to solve image denoising problems, but their performance remains unsatisfactory for most applications. These networks are trained with synthetic noise distributions that do not accurately reflect the noise captured by image sensors. Some datasets of clean-noisy image pairs have been introduced but they are usually meant for benchmarking or specific applications. We introduce the Natural Image Noise Dataset (NIND), a dataset of DSLR-like images with varying levels of ISO noise which is large enough to train models for blind denoising over a wide range of noise. We demonstrate a denoising model trained with the NIND and show that it significantly outperforms BM3D on ISO noise from unseen images, even when generalizing to images from a different type of camera. The Natural Image Noise Dataset is published on Wikimedia Commons such that it remains open for curation and contributions. We expect that this dataset will prove useful for future image denoising applications.