Spatially adaptive context-based wavelet shrinkage for borescope image denoising

Peng Ding; Qi Shuang Ma; Chang You Li; Hong Yu Yao

doi:10.1117/12.716903

24 October 2006 Spatially adaptive context-based wavelet shrinkage for borescope image denoising

Peng Ding, Qi Shuang Ma, Chang You Li, Hong Yu Yao

Proceedings Volume 6357, Sixth International Symposium on Instrumentation and Control Technology: Signal Analysis, Measurement Theory, Photo-Electronic Technology, and Artificial Intelligence; 63570J (2006) https://doi.org/10.1117/12.716903
Event: Sixth International Symposium on Instrumentation and Control Technology, 2006, Beijing, China

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.

Citation Download Citation

Peng Ding, Qi Shuang Ma, Chang You Li, and Hong Yu Yao "Spatially adaptive context-based wavelet shrinkage for borescope image denoising", 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); https://doi.org/10.1117/12.716903

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