Spotlight Poster

Transition-constant Normalization for Image Enhancement

Jie Huang · man zhou · Jinghao Zhang · Gang Yang · Mingde Yao · Chongyi Li · Zhiwei Xiong · Feng Zhao

Great Hall & Hall B1+B2 (level 1) #111
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Wed 13 Dec 8:45 a.m. PST — 10:45 a.m. PST

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}}.

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