Nonsubsampled Contourlet Transform Based Infrared Image Super-Resolution by Using Sparse Dictionary and Residual Dictionary

Abstract

Due to the limitation of hardware, Infrared (IR) image has low-resolution (LR) and poor visual quality. To enhance the Infrared image’s resolution, super-resolution (SR) is a good solution. However, the conventional SR methods have some drawbacks. Firstly, the trained dictionary is an unstructured dictionary, which may lead to worse results. Secondly, the representation of the image is too simple to effectively represent image. Finally, only one high-resolution (HR)-LR dictionary pair is adopted to infer HR IR image. However one HR-LR dictionary pair is not good enough to obtain good results. To resolve these problems, in this paper, firstly, the sparse dictionary is introduced into the IR image SR to get better results. Secondly, nonsubsampled contourlet transform (NSCT) is employed to obtain a better representation of IR image. Finally, to achieve better results, two HR–LR sparse dictionary pairs, which consists of a primitive sparse dictionary pair and a residual sparse dictionary pair, instead of one HR-LR dictionary pair are adopted. The experiment results indicate that the subjective visual effect and objective evaluation acquire excellent performance in the proposed method. Besides, this method is superior to other methods.