Low-light image enhancement based on joint convolutional  sparse representation

Jie Zhang; Yanhou Zhang; Pucheng Zhou; Yusheng Han; Mogen Xue

doi:10.1117/12.2511946

7 November 2018 Low-light image enhancement based on joint convolutional sparse representation

Jie Zhang, Yanhou Zhang, Pucheng Zhou, Yusheng Han, Mogen Xue

Author Affiliations +

Proceedings Volume 10832, Fifth Conference on Frontiers in Optical Imaging Technology and Applications; 108321T (2018) https://doi.org/10.1117/12.2511946
Event: Fifth Conference on Frontiers in Optical Imaging Technology and Applications, 2018, Changchun, China

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.

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Jie Zhang, Yanhou Zhang, Pucheng Zhou, Yusheng Han, and Mogen Xue "Low-light image enhancement based on joint convolutional sparse representation ", Proc. SPIE 10832, Fifth Conference on Frontiers in Optical Imaging Technology and Applications, 108321T (7 November 2018); https://doi.org/10.1117/12.2511946

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