This paper presents a wavelet-domain Hidden Markov Tree(HMT)-based color image superresolution algorithm. Because there exists correlations among the three channels of a RGB color image

a channel by channel superresolution method almost certainly leads to color distortions. In order to solve this problem

first the low-resolution color image is converted into a gray-scale image using the spatially-adaptive approach presented in this paper and the resulting gray-scale image must reflect the human perception of edges in the color image; then by superresolving this gray-scale image

a high-resolution image is obtained; finally

wavelet-domain HMT-based image superresolutions are performed for the three channels of the low-resolution color image using the same posterior state probabilities

which reflect the hidden states of the wavelet coefficients of the high-resolution grayscale image obtained before

and thus the resulting high-resolution color image is what we desired. Because the correlations among the three channels of a RGB color image are considered

there are no color distortions in the reconstructed high-resolution image. Experimental results show that the reconstructed color images have high PSNR and are of high visual quality.