提出了一种基于小波域隐马尔可夫树(HMT)模型的RGB彩色图像超分辨率算法。由于彩色图像3个通道之间具有的相关性，对3个通道分别进行独立的超分辨率重构会导致严重的色彩失真。为解决这个问题，首先通过自适应图像变换由彩色图像得到一幅能反映人类视觉感受的灰度图像；然后对此灰度图像进行超分辨率重构获得一幅高分辨率灰度图像；最后按照这一高分辨率灰度图像确定的小波系数后验状态概率对彩色图像的3个通道分别进行超分辨率重构从而获得一幅高分辨率彩色图像。由于该算法协调了彩色图像3个通道的超分辨率，因此重构出的高分辨率彩色图像避免了色彩失真。实验结果证明该算法重构出的高分辨率彩色图像具有较高的信噪比和非常好的视觉效果。

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.