Image Super-Resolution Based on MCA and Wavelet-Domain HMT

In this paper we propose an image super-resolution algorithm using The Morphological Component Analysis(MCA) and wavelet-domain Hidden Markov Tree(HMT) model. The MCA is a useful method for signal decomposing, using proper basis, we could separate features contained in a signal when these features present different morphological aspects. Wavelet-domain HMT models the dependencies of multiscale wavelet coefficients through the state probabilities of wavelet coefficients. In this paper, we first decompose an image into texture and piecewise smooth (cartoon) parts, then enlarge the cartoon part with interpolation, because wavelet-domain HMT accurately characterizes the statistics of real-world images, we specify it as the prior distribution and then formulate the image super-resolution problem as a constrained optimization problem to acquire the enlarged texture part, finally we get a fine result.