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Proceedings Paper

Low-light image enhancement based on joint convolutional sparse representation
Author(s): Jie Zhang; Yanhou Zhang; Pucheng Zhou; Yusheng Han; Mogen Xue
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Paper Abstract

Low-light image enhancement is a challenging problem in the field of computer vision. In order to obtain more pleasing enhancement results, a low-light image enhancement method via joint convolutional sparse representation is proposed. The method is based on the Retinex theory and improves the problem of insufficient constraints. More concretely, when estimating illumination, the joint convolution sparse representation is proposed as structure and texture constraints to obtain a structural image severed as illumination. Then, the adaptive gradient constraint is used to enhance the details of the reflection image. Experiments on a number of challenging low-light images are present to reveal the efficacy of our method and show its superiority over several state-of-the-arts on both subjective and objective assessments.

Paper Details

Date Published: 7 November 2018
PDF: 6 pages
Proc. SPIE 10832, Fifth Conference on Frontiers in Optical Imaging Technology and Applications, 108321T (7 November 2018); doi: 10.1117/12.2511946
Show Author Affiliations
Jie Zhang, Army Artillery and Air Defense Forces Academy of the PLA (China)
Yanhou Zhang, Army Artillery and Air Defense Forces Academy of the PLA (China)
Pucheng Zhou, Army Artillery and Air Defense Forces Academy of the PLA (China)
Yusheng Han, Army Artillery and Air Defense Forces Academy of the PLA (China)
Mogen Xue, Army Artillery and Air Defense Forces Academy of the PLA (China)


Published in SPIE Proceedings Vol. 10832:
Fifth Conference on Frontiers in Optical Imaging Technology and Applications
Junhao Chu; Wenqing Liu; Huilin Jiang, Editor(s)

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