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Volume 13
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10.3390/app131810230
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Article

A Low-Brightness Image Enhancement Algorithm Based on Multi-Scale Fusion

by
Enqi Zhang
1,2,
Lihong Guo
1,2,
Junda Guo
3,
Shufeng Yan
2,
Xiangyang Li
4 and
Lingsheng Kong
1,*
1
Changchun Institute of Optics, Fine Mechanics and Physics, Chinese Academy of Sciences, Changchun 130033, China
2
University of Chinese Academy of Sciences, Beijing 100049, China
3
College of Physics, Jilin University, Changchun 130012, China
4
Changchun UP Optotech Co., Ltd., Changchun 130031, China
*
Author to whom correspondence should be addressed.
Appl. Sci. 2023, 13(18), 10230; https://doi.org/10.3390/app131810230
Submission received: 24 August 2023 / Revised: 6 September 2023 / Accepted: 11 September 2023 / Published: 12 September 2023
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Abstract

Images captured in low-brightness environments typically have low brightness, low contrast, and high noise levels, which significantly affect the overall image quality. To improve the image quality, a low-brightness image enhancement algorithm based on multi-scale fusion is proposed. First, a novel brightness transformation function is used for the generation of two images with different brightnesses. Then, the illumination estimation technique is used to construct a weight matrix, which facilitates the extraction of advantageous features from each image. Finally, the enhanced image is obtained by the fusion of two images using the weight matrix and the pyramid reconstruction algorithm. The proposed method has a better enhancement effect as shown by the experimental results. Compared to other image enhancement algorithms, it has lower evaluation values in the natural image quality evaluator (NIQE) and lightness order error (LOE) indices. The lowest average NIQE value of the proposed algorithm in each dataset is 2.836. This further demonstrates its superior performance.
Keywords: brightness enhancement; image fusion; image processing; Laplacian pyramid brightness enhancement; image fusion; image processing; Laplacian pyramid

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MDPI and ACS Style

Zhang, E.; Guo, L.; Guo, J.; Yan, S.; Li, X.; Kong, L. A Low-Brightness Image Enhancement Algorithm Based on Multi-Scale Fusion. Appl. Sci. 2023, 13, 10230. https://doi.org/10.3390/app131810230

AMA Style

Zhang E, Guo L, Guo J, Yan S, Li X, Kong L. A Low-Brightness Image Enhancement Algorithm Based on Multi-Scale Fusion. Applied Sciences. 2023; 13(18):10230. https://doi.org/10.3390/app131810230

Chicago/Turabian Style

Zhang, Enqi, Lihong Guo, Junda Guo, Shufeng Yan, Xiangyang Li, and Lingsheng Kong. 2023. "A Low-Brightness Image Enhancement Algorithm Based on Multi-Scale Fusion" Applied Sciences 13, no. 18: 10230. https://doi.org/10.3390/app131810230

APA Style

Zhang, E., Guo, L., Guo, J., Yan, S., Li, X., & Kong, L. (2023). A Low-Brightness Image Enhancement Algorithm Based on Multi-Scale Fusion. Applied Sciences, 13(18), 10230. https://doi.org/10.3390/app131810230

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