  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.
