Multiscale Low-Light Image Enhancement Network With Illumination Constraint

• Published in: IEEE transactions on circuits and systems for video technology Vol. 32; no. 11; pp. 7403 - 7417
• Main Authors: Fan, Guo-Dong, Fan, Bi, Gan, Min, Chen, Guang-Yong, Chen, C. L. Philip
• Format: Journal Article
• Language: English
• Published: New York IEEE 01.11.2022; The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
• Subjects: Atmospheric modeling; Color; Computer vision; Deep learning; Distortion; Exposure; Feature extraction; Histograms; Illumination; Image color analysis; Image enhancement; image processing; Light; Light levels; Lighting; Low-light image enhancement; Res2Net; SENet; Source code; Task analysis; U-Net; Visibility; Visual effects

Images captured under low-light environments typically have poor visibility, affecting many advanced computer vision tasks. In recent years, there have been some low-light image enhancement models based on deep learning, but they have not been able to effectively mine the deep multiscale features in...