首页|More Than Lightening:A Self-Supervised Low-Light Image Enhancement Method Capable for Multiple Degradations
More Than Lightening:A Self-Supervised Low-Light Image Enhancement Method Capable for Multiple Degradations
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国家科技期刊平台
NETL
NSTL
万方数据
Low-light images suffer from low quality due to poor lighting conditions,noise pollution,and improper settings of cameras.To enhance low-light images,most existing methods rely on normal-light images for guidance but the collection of suitable normal-light images is difficult.In contrast,a self-supervised method breaks free from the reliance on normal-light data,resulting in more convenience and better generalization.Existing self-supervised methods primarily focus on illumination adjust-ment and design pixel-based adjustment methods,resulting in remnants of other degradations,uneven brightness and artifacts.In response,this paper proposes a self-supervised enhancement method,termed as SLIE.It can handle multiple degradations including illumination attenuation,noise pollution,and color shift,all in a self-supervised manner.Illumination attenuation is estimated based on physical principles and local neighborhood information.The removal and correction of noise and color shift removal are solely realized with noisy images and images with color shifts.Finally,the comprehensive and fully self-supervised approach can achieve better adaptability and generalization.It is applicable to various low light conditions,and can reproduce the original color of scenes in natural light.Extensive experiments conducted on four public datasets demonstrate the superiority of SLIE to thirteen state-of-the-art methods.Our code is available at https://github.com/hanna-xu/SLIE.
Color correctionlow-light image enhancementself-supervised learning
Han Xu、Jiayi Ma、Yixuan Yuan、Hao Zhang、Xin Tian、Xiaojie Guo
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Elec-tronic Information School,Wuhan University,Wuhan 430072,China
Department of Electronic Engineering,Chi-nese University of Hong Kong,Hong Kong 999077,China
College of Intelligence and Computing,Tianjin Univer-sity,Tianjin 300350,China
国家科技期刊平台
NSTL
万方数据
