Low-light image enhancement via layer decomposition and optimization

Ying Xue; Pucheng Zhou; Mogen Xue

doi:10.1117/12.2589353

27 January 2021 Low-light image enhancement via layer decomposition and optimization

Ying Xue, Pucheng Zhou, Mogen Xue

Proceedings Volume 11720, Twelfth International Conference on Graphics and Image Processing (ICGIP 2020); 117201B (2021) https://doi.org/10.1117/12.2589353
Event: Twelfth International Conference on Graphics and Image Processing, 2020, Xi'an, China

Abstract

When capturing images in low-light conditions, the images are often degraded with low visibility and severe noise. To improve the visual quality and repress the noise simultaneously, a kind of low-light image enhancement method via layer decomposition and optimization was proposed. Firstly, the low-light image was smoothed via iterative least squares thus we could get the noise-free basic layer. Secondly, by means of subtraction of the original image and the basic layer we could get the detailed layer. Then we enhance the basic layer via variational Retinex-based method. At the meantime, we weaken the noise of the detailed layer by non-subsampled shearlet transform. Finally, we could obtain the enhanced image by fusion of the optimized basic layer and detailed layer. Experimental results of a number of low-light images reveal the efficiency of the proposed method and show its superiority over several state-of-the-arts.

Citation Download Citation

Ying Xue, Pucheng Zhou, and Mogen Xue "Low-light image enhancement via layer decomposition and optimization", Proc. SPIE 11720, Twelfth International Conference on Graphics and Image Processing (ICGIP 2020), 117201B (27 January 2021); https://doi.org/10.1117/12.2589353

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