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.
@cite_3: Imaging in low light is challenging due to low photon count and low SNR. Short-exposure images suffer from noise, while long exposure can induce blur and is often impractical. A variety of denoising, deblurring, and enhancement techniques have been proposed, but their effectiveness is limited in extreme conditions, such as video-rate imaging at night. To support the development of learning-based pipelines for low-light image processing, we introduce a dataset of raw short-exposure low-light images, with corresponding long-exposure reference images. Using the presented dataset, we develop a pipeline for processing low-light images, based on end-to-end training of a fully-convolutional network. The network operates directly on raw sensor data and replaces much of the traditional image processing pipeline, which tends to perform poorly on such data. We report promising results on the new dataset, analyze factors that affect performance, and highlight opportunities for future work.
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.