Research Area


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


  • Sony (China) Limited


  • ICME


  • 2021


Rethinking Noise Modeling in Extreme Low-Light Environments

View Publication


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.