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Proceedings Paper

Low-light image enhancement via coupled dictionary learning and extreme learning machine
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Paper Abstract

In this paper, a novel enhancement algorithm for low-light images captured under low illumination conditions is proposed. More concretely, we design a method firstly to synthesize low-light images as training datasets. Then preclustering is conducted to separate training data into several groups by a coupled Gaussian mixture model. For each group, we adopt a coupled dictionary learning approach to train the low-light and normal-light dictionary pair jointly, and the statistical dependency of the sparsity coefficients is captured via Extreme Learning Machine simultaneously. Besides, we use a multi-phase dictionary learning strategy to enhance the robustness of our method. Experimental results show that proposed method is superior to existing methods.

Paper Details

Date Published: 29 October 2018
PDF: 5 pages
Proc. SPIE 10836, 2018 International Conference on Image and Video Processing, and Artificial Intelligence, 108360J (29 October 2018); doi: 10.1117/12.2514014
Show Author Affiliations
Jie Zhang, Anhui Province Key Lab. of Polarized Imaging Detection Technology (China)
Academy of Artillery and Air Defence Forces (China)
Pucheng Zhou, Anhui Province Key Lab. of Polarized Imaging Detection Technology (China)
Academy of Artillery and Air Defence Forces (China)
Mogen Xue, Anhui Province Key Lab. of Polarized Imaging Detection Technology (China)
Academy of Artillery and Air Defence Forces (China)


Published in SPIE Proceedings Vol. 10836:
2018 International Conference on Image and Video Processing, and Artificial Intelligence
Ruidan Su, Editor(s)

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