Image superresolution is referred to as generating a high-resolution image from one or several low-resolution images. Generally speaking, a low-resolution image can be considered as the degraded version of the high-resolution image and the degradations are characterized by blurring and downsampling. Like image restoration, image superresolution is an ill-conditioned problem. So extracting the original high-resolution image from low-resolution images is impossible. The only thing that can be done is to find a high-resolution image which is approximate to the original image in some sense. Now, there are two types of algorithms for image superresolution: one is based reconstruction and the other is based learning. According to the reconstruction-based approach, the prior knowledge about the original high-resolution image is introduced and combined with the degradation model to search an estimate of the original high-resolution image. The learning-based method can also be called as image analogies. First, the relations between high-resolution images and the corresponding degraded low-resolution images is learned from training data; then, apply this learned relations to the low-resolution images and obtain the high-resolution images. Obviously, the key of the former approach lies in the introduction of prior knowledge. Traditionally, natural images are assumed to have smoothness of different order, for example, of one or two order. The improved image models include introduction of line process, the half-quadratic model, total variation and wavelet-domain generalized Gaussian distribution. Unfortunately, there is one thing in common to all these prior models: the reconstructed high-resolution images are severely blurred, especially around edges. To overcome this drawback, the wavelet-domain hidden Markov tree-structured (HMT) model is adopted as the prior model of natural images. Modeling the wavelet coefficients of natural images ,by the Markov dependencies of the hidden stare variables and Gaussian mixtures, wavelet-domain HMT precisely characterizes the: statistics of wavelet transform of natural images.By introducing wavelet-domain HMT, image superresolution is converted into a constrained or unconstrained optimization problem. The state probabilities of wavelet coefficients can detect the multiscale edges in images, so different regions of an image can be processed adaptively by partitioning the image into smooth regions and edges and thus edges are properly preserved. Because color images are widely used in many applications, color image superresolution is discussed in this paper. Due to the correlations among three channels of color images, any channel-by-channel superresolution method will result in color distortions. In this paper, a data-fusion-based algorithm is proposed. According to this approach, a gray-scale image is obtained by fusing three channels and then coordinate the superresolution rec
修改评论