Article ID | Journal | Published Year | Pages | File Type |
---|---|---|---|---|
4970400 | Signal Processing: Image Communication | 2017 | 12 Pages |
Abstract
This paper presents a novel variational framework for low-light image enhancement. The proposed enhancement algorithm simultaneously performs brightness enhancement and noise reduction using a variational optimization. An edge-preserved noise reduction is performed by minimizing the total variation constraint term in the energy function. In addition, the proposed method estimates the optimal transmission map to restore the low-light image by minimizing the â2-norm smoothness and data-fidelity terms. To minimize the proposed energy functional, the proposed method splits the â1-derivative term under the split Bregman iteration framework. The performance of the proposed method is evaluated using both simulated and natural low-light images. Experimental results show that the proposed enhancement method can significantly improve the quality of the low-light images without noise amplification.
Related Topics
Physical Sciences and Engineering
Computer Science
Computer Vision and Pattern Recognition
Authors
Seungyong Ko, Soohwan Yu, Seonhee Park, Byeongho Moon, Wonseok Kang, Joonki Paik,