NODE: Extreme Low Light Raw Image Denoising using a Noise Decomposition Network
This addresses the critical challenge of noise removal in extreme low-light imaging for photography and computer vision applications, representing a novel method for a known bottleneck.
The paper tackled denoising extreme low-light raw images by proposing a multi-task deep neural network called NODE that explicitly estimates defective pixel, Gaussian, and Poisson noise, resulting in more effective denoising than state-of-the-art techniques.
Denoising extreme low light images is a challenging task due to the high noise level. When the illumination is low, digital cameras increase the ISO (electronic gain) to amplify the brightness of captured data. However, this in turn amplifies the noise, arising from read, shot, and defective pixel sources. In the raw domain, read and shot noise are effectively modelled using Gaussian and Poisson distributions respectively, whereas defective pixels can be modeled with impulsive noise. In extreme low light imaging, noise removal becomes a critical challenge to produce a high quality, detailed image with low noise. In this paper, we propose a multi-task deep neural network called Noise Decomposition (NODE) that explicitly and separately estimates defective pixel noise, in conjunction with Gaussian and Poisson noise, to denoise an extreme low light image. Our network is purposely designed to work with raw data, for which the noise is more easily modeled before going through non-linear transformations in the image signal processing (ISP) pipeline. Quantitative and qualitative evaluation show the proposed method to be more effective at denoising real raw images than state-of-the-art techniques.