Rethinking Noise Modeling in Extreme Low-Light Environments

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....

Full description

Saved in:
Bibliographic Details
Main Authors: Wang, Jing, Yu, Yitong, Wu, Songtao, Lei, Chang, Xu, Kuanhong
Format: Conference Proceeding
Language:English
Subjects:
Calibration
Image color analysis
Low-light denoising
neural network
Noise measurement
noise model
Sensors
Signal processing algorithms
Streaming media
Training
Online Access:Request full text
Tags: Add Tag
No Tags, Be the first to tag this record!
Description
Summary: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.
ISSN:1945-788X
DOI:10.1109/ICME51207.2021.9428259