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

Abstract

Due to the limitation of hardware, Infrared (IR) image has low-resolution (LR) and poor visual quality. Infrared image super-resolution (SR) is a good solution for this problem. 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. To resolve these problems, in this paper, firstly, the sparse dictionary is introduced into the IR image SR to get better results. Secondly, nonsubsampled contour let transform (NSCT) is employed in the proposed method to obtain a better representation of IR image. The experiment results indicate that the subjective visual effect and objective evaluation are acquired excellent performance in the proposed method. Besides, this method is superior to other methods in the paper.