MHMM-based computed radiography image-denoising method
Bo Wang · Optics and Precision Engineering · 2002
In the process of computed radiography imaging, a lot of noises will be brought into the system. Therefore, only by knowing their resources, characteristics and relationship with signals, can the authors smooth them. On the basis of analyzing the computed radiography system in detail, the authors point out that there are two kinds of noises affecting the quality of a computed radiography image: Gaussian white noise and Poisson noise. Firstly, the statistical characteristics of wavelet coefficients and Gaussian white noise are summarized. Then, they are described with the mixture Gaussian model and hidden Markov model (HMM), which can fit the dependency of wavelet coefficients between scales. Finally, the novel algorithm developed by the authors is compared with other wavelet-based denoising methods.