HMRF-Based Adaptive Super-Resolution Image Reconstruction Algorithm
Yuan Fen · Microcomputer Information · 2009
In the MAP super-resolution image reconstruction algorithm, the image discontinuities and details can be preserved better if Huber-Markov Random Fields (HMRF) is used as prior models compared with Gaussian prior models. There is no explicit method for the choice of Huber function parameter, or threshold T in the previous studies. In this paper, an adaptive MAP super-resolution reconstruction algorithm is proposed, in which the parameter T can be determined automatically and updated using the partially reconstructed result at each iteration step. The results of the experiments indicate that the proposed algorithm can not only make an automatic choice of the parameter T and get the high resolution reconstruction image expectedly, but also can preserve the edges and details of the image effectively.