Transition-constant Normalization for Image Enhancement

Jie Huang, Man Zhou, JingHao Zhang, Gang Yang, Mingde Yao, Chongyi Li, Zhiwei Xiong, Feng Zhao

Keywords: Image Enhancement, Normalization, Image Restoration

Abstract:

Normalization techniques that capture image style by statistical representation have become a popular component in deep neural networks.
Although image enhancement can be considered as a form of style transformation, there has been little exploration of how normalization affect the enhancement performance.
To fully leverage the potential of normalization, we present a novel Transition-Constant Normalization (TCN) for various image enhancement tasks.
Specifically, it consists of two streams of normalization operations arranged under an invertible constraint, along with a feature sub-sampling operation that satisfies the normalization constraint.
TCN enjoys several merits, including being parameter-free, plug-and-play, and incurring no additional computational costs.
We provide various formats to utilize TCN for image enhancement, including seamless integration with enhancement networks, incorporation into encoder-decoder architectures for downsampling, and implementation of efficient architectures.
Through extensive experiments on multiple image enhancement tasks, like low-light enhancement, exposure correction, SDR2HDR translation, and image dehazing, our TCN consistently demonstrates performance improvements.
Besides, it showcases extensive ability in other tasks including pan-sharpening and medical segmentation.
The code is available at \textit{\textcolor{blue}{https://github.com/huangkevinj/TCNorm}}.

Submission Number: 1166