Wavelet-Domain HMT-Based Image Superresolution

Wavelet-domain hidden Markov tree (HMT) models the dependencies of multiscale wavelet coefficients through the state probabilities of the wavelet coefficients, whose distribution densities can be approximated by the Gaussian mixture. Because wavelet-domain HMT accurately characterizes the statistics of real-world images, the presented algorithm specifies the prior distribution of the real-world image through wavelet-domain HMT. Cycle-Spinning technique is used to suppress the artifacts that may exist in the reconstructed high-resolution images. Experimental results show that the algorithm properly retrieves various kinds of edges and the reconstructed images have high PNSR. Quantitative error analyses are provided and several images are shown for subjective assessment.