Research Area

Author

  • Jing Wang, Yitong Yu*, Songtao Wu, Chang Lei*, Kuanhong Xu

Company

  • Sony (China) Limited

Venue

  • ICME

Date

  • 2021

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Rethinking Noise Modeling in Extreme Low-Light Environments

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Abstract

Recent research has shown Convolutional Neural Networks (CNNs) trained in a fully-supervised fashion achieve promising performance on extreme low-light image denoising task. However, a large amount of “noisy-clean” image pairs are required to train a network, which are difficult to obtain. In this paper, we propose a compact yet effective noise model to generate synthetic noisy images for training. Especially, we address the severe color distortion problem in low-light images by identifying a novel noise component, black calibration error, as its physical origin. We prove that a small error at the sensing stage will strongly affect the following in-camera signal processing (ISP) pipeline and eventually lead to color bias. Experiment results demonstrate that the proposed model is superior in preserving perceptual quality and achieves state-of-the-art performance among existing noise synthesis methods.

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