Sparse coding based Single Image Super-Resolution (SISR) technology has proved to be effective in generating High-Resolution (HR) image from a single Low-Resolution (LR) image. However, unsuitable codebook will bring unexpected structural details in the resultant HR images. In this paper, in order to find more reliable image features for formulating codebook, we propose new Curvelet Support Value Filters (CSVFs) for multiscale structural features extraction. By defining a local multi-scale and multi-directional Curvelet function to approximate images and casting a structural risk minimization constraint in this approximation, we can derive a set of Curvelet support values to reveal the most salient local scale-location-direction information of images. Then these features are used to construct more reliable codebook, and sparse coding based recovery is performed at multiple scale and directions via two coupled example datasets, to recover HR images. Some experiments are taken on realizing a 3X amplification of natural images, and the recovered results suggest its efficiency and superiority to its counterparts.
['Shuyuan Yang', 'Jiren Zhang', 'Shun Cui', 'Min Wang', 'Licheng Jiao']
Curvelet Support Value Filters (CSVFs) for image super-resolution