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Proceedings Paper

Image denoising by principal basis analysis
Author(s): Hong Sun; Cheng-Wei Sang; Cheng-Guang Liu
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Paper Abstract

This article addresses the image denoising problem in the situations of strong noise. The method we propose is intended to preserve faint signal details under these difficult circumstances. The new method we introduce, called principal basis analysis, is based on a novel criterion: the reproducibility which is an intrinsic characteristic of the geometric regularity in natural images. We show how to measure reproducibility. Then we present the principal basis analysis method, which chooses, in sparse representation of the signal, the components optimizing the reproducibility degree to build a so-called principal basis. With this principal basis, we show that a noise-free reconstruction may be obtained. As illustrations, we apply the principal signal basis to image denoising for natural images with details in low signal-to-noise ratio, showing performance better than some reference methods.

Paper Details

Date Published: 17 December 2015
PDF: 6 pages
Proc. SPIE 9811, MIPPR 2015: Multispectral Image Acquisition, Processing, and Analysis, 98110K (17 December 2015); doi: 10.1117/12.2205535
Show Author Affiliations
Hong Sun, Wuhan Univ. (China)
Telecom ParisTech (France)
Cheng-Wei Sang, Wuhan Univ. (China)
Cheng-Guang Liu, Wuhan Univ. (China)


Published in SPIE Proceedings Vol. 9811:
MIPPR 2015: Multispectral Image Acquisition, Processing, and Analysis
Jinxue Wang; Zhiguo Cao; Jayaram K. Udupa; Henri Maître, Editor(s)

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