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Proceedings Paper

Spatially adaptive context-based wavelet shrinkage for borescope image denoising
Author(s): Peng Ding; Qi Shuang Ma; Chang You Li; Hong Yu Yao
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Paper Abstract

Using borescope equipment to inspect the inside of turbine engines is an important technology to the daily damage detection of aeronautic engine. Because the borescope image that we observe is based upon point light, and the quantum nature of light is not ideal enough, borescope image acquired through charge-coupled device (CCD) is contaminated by white Gaussian noise. Towards this, a kind of spatially adaptive context-based wavelet shrinkage borescope image denoising method was presented. The spatially adaptive wavelet thresholding was selected based on context modeling, which was used in our prior borescope image compression coder to adapt the probability. Each wavelet coefficient was modeled as a Gibbs field distribution. Context modeling was used to estimate the thresholding for each coefficient. This method was based on an overcomplete non-subsampled wavelet representation, which yielded better results than the orthogonal transform. Experimental results show that spatially adaptive wavelet thresholding yields significantly improved visual quality as well as lower mean squared error (MSE) compared to the method of Chang.

Paper Details

Date Published: 24 October 2006
PDF: 5 pages
Proc. SPIE 6357, Sixth International Symposium on Instrumentation and Control Technology: Signal Analysis, Measurement Theory, Photo-Electronic Technology, and Artificial Intelligence, 63570J (24 October 2006); doi: 10.1117/12.716903
Show Author Affiliations
Peng Ding, BeiHang Univ. (China)
Qi Shuang Ma, BeiHang Univ. (China)
Chang You Li, BeiHang Univ. (China)
Hong Yu Yao, General Administration of Civil Aviation of China (China)


Published in SPIE Proceedings Vol. 6357:
Sixth International Symposium on Instrumentation and Control Technology: Signal Analysis, Measurement Theory, Photo-Electronic Technology, and Artificial Intelligence
Jiancheng Fang; Zhongyu Wang, Editor(s)

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