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A Hybrid De-Noising Method on LASCA Images of Blood Vessels
Graduate School of Engineering Hiroshima University, Hiroshima, Japan
Wuhan National Laboratory for Optoelectronics, Huazhong University of Science and Technology, Wuhan, China
Graduate School of Engineering Hiroshima University, Hiroshima, Japan
Wuhan National Laboratory for Optoelectronics, Huazhong University of Science and Technology, Wuhan, China
- 1 Graduate School of Engineering Hiroshima University, Hiroshima, Japan
- 2 Wuhan National Laboratory for Optoelectronics, Huazhong University of Science and Technology, Wuhan, China
- 3 Graduate School of Engineering Hiroshima University, Hiroshima, Japan
- 4 Wuhan National Laboratory for Optoelectronics, Huazhong University of Science and Technology, Wuhan, China
Journal of Signal and Information Processing·Volume 03 (2012)·Pages 92–97·Published 28 February 2012·DOI10.4236/jsip.2012.31012
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Abstract
A de-noising approach is proposed that based on the combination of wiener filtering, nonlinear filtering and wavelet fusion, which de-noise the LASCA (LAser Speckle Contrast Analysis) image of blood vessels in Small Animals. The approach first performs laser spectral contrast analysis on brain blood flow in rats, get their spatial and temporal contrast images. Then, a de-noising filtering method is proposed to deal with noise in LASCA. The image restoration is achieved by applying the proposed admixture filtering, and the subjective estimation and objective estimation are given to the de-noising images. As our experimental results shown, the proposed method provides clearer subjective sense and improved to over 25 db for PSNR.
KeywordsBrain Blood FlowWavelet FusionHybrid FilteringLaser Speckle Contrast Imaging
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