A New Image Super-Resolution Method in the Wavelet Domain

In this paper, we propose a novel single image super-resolution method based on MAP statistical reconstruction. Our approach takes the wavelet domain Implicit Markov Random Field (IMRF) model as the prior constraint. Specifically, we apply the IMRF model to show the probability distribution of natural images in wavelet domain, and utilize the MAP theory of Bayesian estimation to construct the objective function with this model. Furthermore, we employ steepest descent method to optimize this objective function. In the experiments, we use both Peak Signal to Noise Ratio (PSNR) and visual effect to evaluate our method. The experimental results demonstrate that our method obtains the superior performance in comparison with traditional single image super-resolution approaches.