Research On Image Super-resolution Interpolation Algorithm Using HMT Model In Wavelet Domain

With the development of sensor technology of image acquisition, though the higher resolution image can be obtained, but the production level due to hardware limitations, and how to use the software methods to improve the image resolution is growing concern.The conventional super-resolution image interpolation algorithm due to the limitation of principle, not fully consider the space gradient information and the statistics characteristic of image data, it also can not effectively identify image edge, resulting in blurred edge details or stepped aliasing edge.This paper deals with the super-resolution image interpolation method based on wavelet transform combined with hidden Markov tree (HMT) model. Wavelet coefficients are modeled as the wavelet domain HMT model, using Gaussian mixture model to describe the probability statistical distribution of wavelet coefficients of each sub-band. Use of the tree shape Markov reflected in the structure of the wavelet coefficients across the scales, the wavelet coefficients corresponding to the hidden state modeling. In order to achieve the wavelet coefficients and the HMT model matching using the expectation maximization (EM) algorithm to estimate the HMT model parameters, the super-resolution image interpolation problem is transformed into a constrained optimization problem. The experimental results show that the effect of images with the experimental data, to verify the effectiveness and practicality of the proposed algorithm.