Single Image Super Resolution Algorithm with a New Dictionary Learning Technique K-Eigen Decomposition

Abstract

In this paper, we propose an algorithm to improve some important details of sparse representation based image super resolution (SR) framework. Firstly, a new dictionary learning technique K-Eigen decomposition (K-EIG) is proposed. It improves the classical K-SVD algorithm in dictionary atom updating. K-EIG accelerates the learning process and keeps the similar performance of the learned dictionary. Secondly, image patch classification and edge patches extension are integrated into the SR framework. Two over-complete dictionary-pairs are trained based on K-EIG. In reconstruction, the input low resolution (LR) image is split into patches and each one is classified. The patch type decides which dictionary-pair is chosen. Then the sparse representation coefficient of the LR signal is inferred and the corresponding high resolution (HR) patch can be reconstructed. Experimental results prove that our algorithm can obtain competitive SR performance when compared with some classical methods. Besides, the time-consuming of dictionary-pair learning is lower.