Image Super-resolution Reconstruction Based On MAP Method With Regularization

SR (super-resolution) reconstruction is a restoring method which makes use of a series of LR images to restore an HR image based on image processing technology. Several common SR methods are analyzed. In literature, the motion or PSF (point spread function) is generally supposed as known. However, less study considers the case that both of them are unknown. However, in a real situation, there is only image data and the motion and PSF are unknown. In this situation, how to reconstruct HR image is the key problem the paper deals with. In this paper, a useful SR reconstruction method for unknown global translation and unknown PSF is proposed. For motion estimation, the relation of pixels in an LR (low-resolution) image is used to realize the sub-pixel translation estimation and for specific estimation algorithm, the easily used block-matching algorithm is chosen. For unknown PSF, the necessity of blind deconvolution is discussed. Since ML algorithm is less constraint, it is chosen for blind deconvolution. The outstanding characteristic of the paper is that the only necessity for SR construction is the LR image data showing the same scene. Besides, the SR method proposed is easily applied with less computing complexity. The simulation results have been presented to show the effectiveness of the proposed algorithm.Since the problem of SR reconstruction is unable to be solved by one method, an overall approach is usually needed. Thus in order to improve the restoring efficiency and restoring effect, a lot of methods which are able to be used in SR reconstruction are discussed and compared, such as statistical methods, image processing methods and optimization algorithm etc. Specifically for motion estimation, block matching method is chosen after several motion estimation methods discussed and for blind deconvolution method, a convenient method is chosen from several blind deconvolution methods.Besides, several existing methods are improved in order to reconstruct HR image faster and better. For example, in motion estimate and blind deconvolution, we have improved the existing algorithms, which enhance the efficiency of reconstruction.