LEFB: A new low-light image contrast enhancement algorithm

Bin Wang, Bini Zhang and Jinfang Sheng
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Bin Wang: School of Computer Science and Engineering, Central South University, 943 Lushan Road, Changsha 410083, P. R. China
Bini Zhang: School of Computer Science and Engineering, Central South University, 943 Lushan Road, Changsha 410083, P. R. China
Jinfang Sheng: School of Computer Science and Engineering, Central South University, 943 Lushan Road, Changsha 410083, P. R. China

International Journal of Modern Physics C (IJMPC), 2024, vol. 35, issue 12, 1-18

Abstract: Low-light images are challenging for both human observation and computer vision algorithms due to low visibility. To address this issue, various image enhancement techniques such as dehazing, histogram equalization, and neural network-based methods have been proposed. However, most existing methods often suffer from the problems of insufficient contrast and over-enhancement while enhancing the brightness, which not only affects the visual quality of images but also adversely impacts their subsequent analysis and processing. To tackle these problems, this paper proposes a low-light image enhancement method called LEFB. Specifically, the low-light image is first transformed into the LAB color space, and the L channel controlling brightness is enhanced using a local contrast enhancement algorithm. Then, the enhanced image is further enhanced using an exposure fusion-based contrast enhancement algorithm, and finally, a bilateral filtering function is applied to reduce image edge blurriness. Experimental evaluations are conducted on real datasets with four comparison algorithms. The results demonstrate that the proposed method has superior performance in enhancing low-light images, effectively addressing problems of insufficient contrast and over-enhancement, while preserving fine details and texture information, resulting in more natural and realistic enhanced images.

Keywords: Low-light images; contrast enhancement; LAB; LEFB (search for similar items in EconPapers)
Date: 2024
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DOI: 10.1142/S0129183124501596

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