Coupled K-SVD dictionary training for super-resolution

In the learning based super-resolution (SR), one of the most important issue is how to learn the relationship between the high resolution (HR) and low resolution (LR) images. Sparse representation has provided dictionary learning methods to describe the relationship. This work presents a coupled dictionary training algorithm named coupled K-singular value decomposition (K-SVD) for SR problem. In this algorithm, the best low-rank approximation provided by singular value decomposition (SVD) is utilized to update the LR and HR dictionaries. Experiments demonstrate that our algorithm converges stably and achieves superior SR results.