A survey on image enhancement for Low-light images

Jiawei Guo, Jieming Ma*, Ángel F. García-Fernández, Yungang Zhang, Haining Liang

*Corresponding author for this work

Research output: Contribution to journalReview articlepeer-review

13 Citations (Scopus)


In real scenes, due to the problems of low light and unsuitable views, the images often exhibit a variety of degradations, such as low contrast, color distortion, and noise. These degradations affect not only visual effects but also computer vision tasks. This paper focuses on the combination of traditional algorithms and machine learning algorithms in the field of image enhancement. The traditional methods, including their principles and improvements, are introduced from three categories: gray level transformation, histogram equalization, and Retinex methods. Machine learning based algorithms are not only divided into end-to-end learning and unpaired learning, but also concluded to decomposition-based learning and fusion based learning based on the applied image processing strategies. Finally, the involved methods are comprehensively compared by multiple image quality assessment methods, including mean square error, natural image quality evaluator, structural similarity, peak signal to noise ratio, etc.

Original languageEnglish
Article numbere14558
Issue number4
Publication statusPublished - Apr 2023


  • Deep learning
  • Image enhancement
  • Image processing
  • Low-light images


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