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Bayesian Image Denoising with Mixed Prior Using Hypothesis-Testing Problem  

Eom Il-Kyu (Dept. of Electronics Engineering, Research Institute of Computer, Information and Communication)
Kim Yoo-Shin (Dept. of Electronics Engineering, Pusan National University)
Publication Information
Journal of the Institute of Electronics Engineers of Korea SP / v.43, no.3, 2006 , pp. 34-42 More about this Journal
Abstract
In general, almost information is stored in only a few wavelet coefficients. This sparse characteristic of wavelet coefficient can be modeled by the mixture of Gaussian probability density function and point mass at zero, and denoising for this prior model is peformed by using Bayesian estimation. In this paper, we propose a method of parameter estimation for denoising using hypothesis-testing problem. Hypothesis-testing problem is applied to variance of wavelet coefficient, and $X^2$-test is used. Simulation results show our method outperforms about 0.3dB higher PSNR(peak signal-to-noise ratio) gains compared to the states-of-art denoising methods when using orthogonal wavelets.
Keywords
혼합 프라이어 모델;웨이블릿;베이지안 추정법;잡음 제거;가설-검증 문제;
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