Image resolution enhancement based on wavelet domain HMT and fusion

Image resolution enhancement algorithms based on the estimation of detail wavelet coefficients at high resolution scales have been proposed recently. These algorithms assume that the low resolution image is the approximation sub-band of a higher resolution image and interpolates images by predicting coefficients at finer scales. Hidden Markov Tree (HMT) in the wavelet domain using Gaussian mixture models are capable of accurately modeling the statistical behavior of real world images by exploiting relationships between coefficients in different scales and have shown to produce promising results. However, one drawback of these methods is that, the coefficients to be estimated are generated randomly, so the results are different every time, only one of them is chosen finally. In this paper, we propose an algorithm which fuses these random results together by means of the fusion rules based on area-based standard deviation. This makes the enhanced image more suitable for the human vision and reduces the disorder degree of the image. Experiments demonstrate the effectiveness of the proposed method and show the superiority to previous methods in objective and subjective qualities.