Infrared surveillance image super resolution via group sparse representation

Abstract

Infrared (IR) surveillance image generally has low resolution and signal-to-noise ratios. In this paper, we present a new approach to IR image super resolution via group sparse representation (GS). First based on IR image feature, an algorithm of combining the group orthogonal matching pursuit and K-SVD is proposed to train the dictionaries. The dictionary training can ensure that the corresponding low resolution (LR) and high resolution (HR) image have same GS coefficients. Then the group sparse coefficients of the input LR image are sought to reconstruct the HR image with the trained LR and HR dictionary pair. Experimental results indicate that our method generates sharper results with higher Peak Signal to Noise Ratio (PSNR), and is more robust to noise than several popular methods.