Computer Science ›› 2025, Vol. 52 ›› Issue (10): 168-175.doi: 10.11896/jsjkx.240800057

• Computer Graphics & Multimedia • Previous Articles     Next Articles

Low Light Image Adaptive Enhancement Algorithm Based on Retinex Theory

ZHENG Dichen1, HE Jikai1, LIU Yi2, GAO Fan1, ZHANG Dengyin2   

  1. 1 School of Communication and Information Engineering,Nanjing University of Posts and Telecommunications,Nanjing 210003,China
    2 School of Internet of Things,Nanjing University of Posts and Telecommunications,Nanjing 210003,China
  • Received:2024-08-09 Revised:2024-10-13 Online:2025-10-15 Published:2025-10-14
  • About author:ZHENG Dichen,born in 2000,post-graduate.His main research interests include image processing and computer vision.
    ZHANG Dengyin,born in 1964,Ph.D,researcher,Ph.D supervisor.His main research interests include signal and information processing and information security.
  • Supported by:
    National Natural Science Foundation of China(61872423).

Abstract: Images in real-world environments are often shot under sub-optimal lighting conditions,resulting in insufficient brightness and poor visual experience.Existing low-light image enhancement methods are often complex in structure and focus on improving the visibility of dark areas,but may over-enhance the bright areas of the image and amplify hidden noise.Most methods based on Retinex theory have problems such as excessive noise,loss of details and color distortion,which affect the visual quality of the enhancement results.In order to solve this problem,this paper proposes a low-light image adaptive enhancement algorithm based on Retinex theory,which aims to effectively improve the brightness of the image while restoring the image truly and naturally.Firstly,the low-light image is passed through the projection module to remove noise that is not suitable for Retinex decomposition.Secondly,the decomposition network decomposes the image into an illumination component and a reflection component.Finally,the illumination component is adjusted through an adaptive iterative curve and multiplied with the reflection component to obtain an enhanced image.Experimental results show that compared with other mainstream algorithms,the proposed algorithm has obvious advantages in objective evaluation indicators,especially PSNR and SSIM:tests on the LOL dataset show that PSNR and SSIM reach 19.98 dB and 0.76,respectively,which are 4.9% and 4.1% higher than the suboptimal algorithm;tests on the LSRW dataset show that PSNR and SSIM reach 18.94 dB and 0.58,respectively,which are 1.5% and 7.4% higher than the suboptimal algorithm.On both of the referenced dataset and the non-reference dataset,the brightness of the enhanced image obtained by the proposed algorithm is significantly improved,the colors are more realistic and natural,and the subjective visual effect is better.

Key words: Image enhancement,Low-light,Retinex,Iterative enhancement,Deep learning

CLC Number: 

  • TP391
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