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.

本研究介绍了自然图像噪声数据集（NIND），用于训练盲目去噪模型，此数据集包括具有不同ISO噪声水平的DSLR式图像。我们展示了使用NIND训练的去噪模型，其在ISO噪声上显着优于BM3D，即使推广到来自不同类型的相机的图像也是如此。这个数据集是开放的，可以进行整理和贡献，并可用于未来的图像去噪应用。