Study On Image Super-Resolution Reconstruction Algorithms Based On Sparse Representation

Image super-resolution reconstruction is a digital signal processing technique to estimate a high-resolution image (or image sequence) from a single image or an image sequence of the same scene. Image super-resolution reconstruction technique can break through the hardware limitation of image collection system, use the redundant information and prior knowledge of image itself to enhance image quality by software methods. At present, this technique has been widely used in public safety, telemetry remote sensing, medical imaging and high-definition TV, etc.This paper performs the researches on single frame image super-resolution reconstruction problems using image sparse prior knowledge, mainly in the following three aspects:Firstly, we propose the reconstruction algorithm based on fusion interpolation aiming to overcome the shortcomings of traditional interpolation methods which often cause blur effect in edges.The proposed algorithm firstly divides the image into smooth area and edges using its sparsity in wavelet domain and judes the edges'orientation using gradient priors, then uses spline interpolation for smotch aeras and directional interpolation for image edges.The experiment results show that the proposed algorithm can produce sharp edges.Secondly,because of the single dictionary can not sparsely represent the image patches of different types, we propose the reconstruction algorithm based on sparse representation of classified image patches. The proposed algorithm divides the image patches into smooth patches, edge patches of different orientation and irregular structure patches, and then trains their corresponding dictionary pairs of high and low resolution for image reconstruction. The experiment results show that the proposed algorithm can produce better visiual quality images and has faster speed.Finally, we apply the sparse representation theory of classified image patches to compress sensing image reconstruction, and propose the reconstruction algorithm based on double sparsity of classified image patches and dual tree complex wavelet. The proposed algorithm reconstructs the image by classeified dictionaries first, and then improves its quality by iterative shrinkage using the sparse prior in the dual tree complex wavelet domain. Experiment results show the validity of the algorithm.