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.
See-in-the-Dark (SID) @cite_3 is an image noise dataset that is large enough for training and, to our knowledge, was used in the first successful attempt at denoising images using real image noise. This dataset focused on very low-light photography where the camera-generated JPEG appears black. The authors used a U-Net network architecture to create an end-to-end RAW-to-JPEG pipeline that produces realistic colors, improving on standard processing and BM3D denoised images which still suffer from color bias at high ISO. Our work differs from SID in that we aimed to train a general purpose ( blind") denoiser rather than one that handles a specific condition, such as extremely low light images. We chose to work in sRGB space because handling the whole RAW-to-sRGB pipeline removes some information which may otherwise be useful to the author during development. Moreover, one dataset can then be used with different types of color filter arrays.
@cite_3
1514812871
In this paper we introduce a dataset of uncompressed color images taken with three digital cameras and exhibiting different levels of natural noise due to low-light conditions. For each scene there are on average two low-noise and two high noise images that are aligned at the pixel level both spatially and in intensity. The dataset contains over 100 scenes and more than 400 images, including both RAW formatted images and 8 bit BMP pixel and intensity aligned images. We also introduce a method for estimating the true noise level in each of our images. We use our dataset to analyze three current state of the art denoising algorithms: Active Random Field, BM3D, and Multi-Layer Perceptron. We found that BM3D obtains the best denoising results, however it is the slowest of the three methods with Active Random Field taking only a few seconds and Multi-Later Perceptron and BM3D taking a few minutes to half an hour to denoise a 10 to 18 mega-pixel image.