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Proceedings Paper

Low-light level image de-noising algorithm based on PCA
Author(s): Zhuang Miao; Xiuqin Wang; Panqiang Yin; Dongming Lu
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Paper Abstract

A de-noising method based on PCA (Principal Component Analysis) is proposed to suppress the noise of LLL (Low-Light Level) image. At first, the feasibility of de-noising with the algorithm of PCA is analyzed in detail. Since the image data is correlated in time and space, it is retained as principal component, while the noise is considered to be uncorrelated in both time and space and be removed as minor component. Then some LLL images is used in the experiment to confirm the proposed method. The sampling number of LLL image which can lead to the best de-noising effects is given. Some performance parameters are calculated and the results are analyzed in detail. To compare with the proposed method, some traditional de-noising algorithm are utilized to suppress noise of LLL images. Judging from the results, the proposed method has more significant effects of de-noising than the traditional algorithm. Theoretical analysis and experimental results show that the proposed method is reasonable and efficient.

Paper Details

Date Published: 24 November 2014
PDF: 8 pages
Proc. SPIE 9301, International Symposium on Optoelectronic Technology and Application 2014: Image Processing and Pattern Recognition, 93011E (24 November 2014); doi: 10.1117/12.2072039
Show Author Affiliations
Zhuang Miao, Science and Technology on Low-Light-Level Night Vision Lab. (China)
Xiuqin Wang, The North Electro-optics Group Co., Ltd. (China)
Panqiang Yin, Nanjing Univ. of Science and Technology (China)
Dongming Lu, Nanjing Univ. of Science and Technology (China)


Published in SPIE Proceedings Vol. 9301:
International Symposium on Optoelectronic Technology and Application 2014: Image Processing and Pattern Recognition
Gaurav Sharma; Fugen Zhou; Jennifer Liu, Editor(s)

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