A novel regularized K-SVD dictionary learning based medical image super-resolution algorithm

Recently sparse representations over learned dictionaries have been proven to be a very successful representation method for many image processing applications. In the medical image processing community, image super-resolution has been playing a vital role to make the computer based diagnosis more efficient and accurate. Resolution enhancement through conventional interpolation methods strongly affects the precision of consequent processing steps such as segmentation and registration. In this paper, we propose a novel regularized K-SVD dictionary learning based medical image super-resolution algorithm. First, the dictionary is trained using the modified version of the K-SVD dictionary learning procedure. The sparse coding phase of the K-SVD dictionary learning scheme is then enhanced incorporating a simple and an efficient regularized version of orthogonal matching pursuit. In addition, the dictionary update stage allows for an arbitrary number of atoms at the same time and sparse coefficient vector. In the SR reconstruction procedure, ROMP is adopted to find out for the vector of sparse representation coefficients for the underlying patch. In the final part, mathematical optimization finalizes the SR work effectively. The numerical analysis and experimental simulation prove the feasibility and robustness of our proposed methodology compared with other state-of-the-art algorithms.