Image super-resolution via two stage coupled dictionary learning

Example-based image super-resolution aims to establish a learning model for generating the high resolution image from the coupled training pairs, which is an active study area of mobile media in the recent modern communication. In this paper, we proposed a novel example-based method to address the single image super-resolution problem, where the training pairs are selected from a large amount of natural images. The main idea of our method is to reconstruct the high resolution by a two stage-based scheme. In the first stage, one dictionary is learned to represent the coarse high resolution image from its low version, and the other is trained within the same coding as the coarse image to recover texture details. And then, to further enhance the fine edges in image, a similar dictionary learning scenario are done about the synthesis high resolution image and its fine structure in residual component. Extensive experimental results on some benchmark test images show the advantage of our method compare with other excellent ones.