A face super-resolution approach using shape semantic mode regularization

In actual imaging environment, a variety of factors have an impact on the quality of images, which leads to pixel distortion and aliasing. The traditional face super-resolution algorithm only uses the difference of image pixel values as similarity criterion, which degrades similarity and identification of reconstructed facial images. Image semantic information with human understanding, especially structural information, is robust to the degraded pixel values. In this paper, we propose a face super-resolution approach using shape semantic model. This method describes the facial shape as a series of fiducial points on facial image. And shape semantic information of input image is obtained manually. Then a shape semantic regularization is added to the original objective function. The steepest descent method is used to obtain the unified coefficient. Experimental results demonstrate that the proposed method outperforms the traditional schemes significantly both in subjective and objective quality.