Human Face Super-Resolution Based on NSCT

Abstract

The nonsubsampled contourlet transform (NSCT) is a useful tool for vision and image processing, which has the property of multi-scale, multi-direction, and shift-invariant. In this paper, based on the relations between NSCT coefficients, we propose a novel strategy for single-frame human face super-resolution. Both the high resolution (HR) and low resolution (LR) images in the training set are decomposed beforehand by NSCT. Given a low resolution (LR) image, we first decompose it by NSCT, and then use the locally linear embedding (LLE) to learn the target SR image's NSCT coefficients via our proposed SR strategies. At last we use the inverse transformation to compose the final SR image. Extensive experiments on CAS-PEAL Face Database demonstrate that our SR method outperforms the state-of-art methods both visually and in terms of SSIM and PSNR. Results on real world images further verify the effectiveness and superiority of our method.