An image super-resolution algorithm based on wavelet-domain local gaussian model is proposed. Wavelet-domain local gaussian model approximates the local probability distribution of the wavelet coefficients with a single gaussian function. Because the model adaptively characterizes the local statistics of real-world images

the algorithm presented in this paper specifies the prior distribution of the real-world image through it and converts the image super-resolution problem to a constrained optimization one which can be solved with the conjugate gradient method. Experimental results show that the algorithm properly retrieves various kinds of edges and the PNSR and subjective visual effect of the reconstructed images are improved significantly.