Contourlet image modeling with contextual hidden Markov models

The contourlet transform is a recently developed two-dimensional transform technique. It is reported to be more effective than wavelets in representing smooth curvature details typical of natural images. To fully exploit the potential of contourlets in image processing and analysis applications, appropriate models are needed to describe statistical characteristics of images in the contourlet domain. In this paper, statistical contourlet image modeling techniques have been investigated. A contextual hidden Markov model, which was successfully applied to wavelet image denoising, has been adapted into the contourlet domain. The resulting contourlet contextual HMM has been tested in a denoising application with promising results, which verified its effectiveness in characterizing contourlet images.