Research On Image Super-resolution Reconstruction Algorithm Based On Sparse Representation

Image super-resolution is referring to process low-resolution images by somealgorithms, in order to get high-resolution images. It is one of the most populartopics in many fields, such as images processing, computer vision andpatternfrecognition, etc. Image super-resolution reconstruction algorithms based onsparse representation which were inspired by compressed sensing have achievedbetter reconstruction results and complexity of the algorithm than classic methods.The research of the algorithms has significance in the theoretical research and thepractical application of image super-resolution reconstruction technology. Thisdissertation had comparatively done systematic study to super-resolutionreconstruction and the application of sparse representation in imagesuper-resolution reconstruction. Some of the existing algorithms were improved.Firstly, this dissertation had done study to the common image super-resolutionreconstruction algorithms and the sparse representation theoretical. For the imagesuper-resolution reconstruction algorithms, these algorithms which divided into twotypes (based on reconstruction and based on learning) were introduced in details,and the advantages and disadvantages of these algorithms were analysed. For sparserepresentation theory, this dissertation focused on the solving of ill-posed problemand the designing of over-complete dictionary, and applied these to the imagesuper-resolution reconstruction.Secondly, this dissertation had done study to the structure of over-completedictionary and non-local similarity theory based on classic sparse, and presented anew method of based on clustering for single-image super-resolution reconstruction. First,a high-resolution dictionary was learned from the samples of high-resolutionimages; second, got the representation coefficients of high-resolution image byusing iterative shrinkage solution; finally, used the high-resolution dictionary toreconstruct the low-resolution image.Finally, this dissertation compared the method to the classic methods within noiseenvironment and without noise environment. The results of the simulate experiments showthat this method can effectively improve the effect of the reconstruction and reduce thecomplexity of the algorithm.