Coupled K-SVD Dictionary Learning for Single Image Super-Resolution in Wavelet Domain

This thesis introduces coupled K-Singular Value Decomposition (K-SVD) algorithm in wavelet domain for Single Image Super-Resolution (SISR). In the coupled K-SVD the best low-rank approximation given by the SVD is implemented to update the LR and HR dictionaries which in turn help to enforce the equality of the sparse representation coefficients at two resolution levels. Wavelet domain produces better results due to desirable properties such as persistence across scale, compactness, directionality and analysis in many levels with the addition of redundancy in the sparse representations. Using this approach, one can design multiple structured redundant dictionaries, which can potentially help reduce the number of dictionary atoms. Three pairs of coupled low and high resolution wavelet subband dictionaries are designed. Given the low resolution image one first estimates the sparse representation coefficients using the low resolution dictionary and then reconstructs the high resolution image using the calculated low resolution sparse coefficients and high resolution dictionary. This approach generates HR images that are competitive or even better when compared with the state of the art algorithms. Results are improved in terms of PSNR and SSIM in comparison. Keywords: Coupled K-SVD, Wavelets, Single Image Super-Resolution.