Image Super-Resolution Reconstruction Based on Sparse Representation
Wang Zi-hu · Modern Computer · 2014
Proposes a super-resolution reconstruction approach of single gray image based on sparse representation and multi component-dictionary learning. For each patch of the low-resolution input image, considers a sparse representation to train two dictionaries and then uses the coefficients of this representation to generate the high-resolution output image. In order to improve the quality of the reconstructed image, improves the design method of the sparse dictionary using the method of K-SVD layered image in MCA to extract the components of Texture and Cartoon in the image for dictionary learning and super-resolution reconstruction. The results of simulation experiment show that the method leads to an improvement in PSNR.