The Journal of China Universities of Posts and Telecommunications ›› 2022, Vol. 29 ›› Issue (2): 52-62.doi: 10. 19682/ j. cnki. 1005-8885. 2022. 0001

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Low-light image enhancement algorithm using a residual network with semantic information

DUAN Lian1,   

  • Received:2021-01-19 Revised:2021-07-10 Online:2022-04-26 Published:2022-04-26
  • Supported by:
    This work was supported by the Promotion Plan of Jiangsu Association for Science and Technology (TJ215039), and the Research Foundation of Nanjing University of Posts and Telecommunications (NY219076).


Aiming to solve the poor performance of low illumination enhancement algorithms on uneven illumination images, a low-light image enhancement (LIME) algorithm based on a residual network was proposed. The algorithm constructs a deep network that uses residual modules to extract image feature information and semantic modules to extract image semantic information from different levels. Moreover, a composite loss function was also designed for the process of low illumination image enhancement, which dynamically evaluated the loss of an enhanced image from three factors of color, structure, and gradient. It ensures that the model can correctly enhance the image features according to the image semantics, so that the enhancement results are more in line with the human visual experience. Experimental results show that compared with the state-of-the-art algorithms, the semantic-driven residual low-light  network (SRLLN) can effectively improve the quality of low illumination images, and achieve better subjective and objective evaluation indexes on different types of images.

Key words: image enhancement, convolutional neural network (CNN), residual learning, image semantic

CLC Number: