A Low-Brightness Image Enhancement Algorithm Based on Multi-Scale Fusion
文献类型:期刊论文
作者 | E. Q. Zhang, L. H. Guo, J. D. Guo, S. F. Yan, X. Y. Li and L. S. Kong |
刊名 | Applied Sciences-Basel
![]() |
出版日期 | 2023 |
卷号 | 13期号:18页码:17 |
DOI | 10.3390/app131810230 |
英文摘要 | 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. |
URL标识 | 查看原文 |
语种 | 英语 |
源URL | [http://ir.ciomp.ac.cn/handle/181722/68154] ![]() |
专题 | 中国科学院长春光学精密机械与物理研究所 |
推荐引用方式 GB/T 7714 | E. Q. Zhang, L. H. Guo, J. D. Guo, S. F. Yan, X. Y. Li and L. S. Kong. A Low-Brightness Image Enhancement Algorithm Based on Multi-Scale Fusion[J]. Applied Sciences-Basel,2023,13(18):17. |
APA | E. Q. Zhang, L. H. Guo, J. D. Guo, S. F. Yan, X. Y. Li and L. S. Kong.(2023).A Low-Brightness Image Enhancement Algorithm Based on Multi-Scale Fusion.Applied Sciences-Basel,13(18),17. |
MLA | E. Q. Zhang, L. H. Guo, J. D. Guo, S. F. Yan, X. Y. Li and L. S. Kong."A Low-Brightness Image Enhancement Algorithm Based on Multi-Scale Fusion".Applied Sciences-Basel 13.18(2023):17. |
入库方式: OAI收割
浏览0
下载0
收藏0
其他版本
除非特别说明,本系统中所有内容都受版权保护,并保留所有权利。