Super-Resolution Reconstruction of Face Image with Improved Sparse Constraint

In the most of super-resolution reconstruction algorithms, high-resolution and low-resolution images are assumed in the same manifold space. However, due to distractions, this assumption is not suitable for the practical applications. This paper proposes a novel super-resolution reconstruction algorithm for face images. In order to consider the manifold inconsistency of high-resolution and low-resolution images, a mapping function revealing the relationship between the coefficients of high-resolution and low-resolution images is introduced during the stage of dictionary training. During the stage of image reconstructing, the sparse coefficients of high-resolution images can be calculated by mapping function with the input of sparse coefficients of low-resolution images. To remove the irregularity of image edge of initial high-resolution images, an optimization process is presented based on the self-similar characteristic of face images. Experimental results show that the proposed algorithm can perform better in PSNR and SSIM.