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

Jie Zhang; Pucheng Zhou; Mogen Xue

doi:10.1117/12.2514014

29 October 2018 Low-light image enhancement via coupled dictionary learning and extreme learning machine

Jie Zhang, Pucheng Zhou, Mogen Xue

Proceedings Volume 10836, 2018 International Conference on Image and Video Processing, and Artificial Intelligence; 108360J (2018) https://doi.org/10.1117/12.2514014
Event: 2018 International Conference on Image, Video Processing and Artificial Intelligence, 2018, Shanghai, China

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.

Citation Download Citation

Jie Zhang, Pucheng Zhou, and Mogen Xue "Low-light image enhancement via coupled dictionary learning and extreme learning machine", Proc. SPIE 10836, 2018 International Conference on Image and Video Processing, and Artificial Intelligence, 108360J (29 October 2018); https://doi.org/10.1117/12.2514014

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