Reconstruction of super-resolution facial image based on local pixel embedding

The local linear embedding relationship of pixels was used as prior to constrain the reconstruction of HR facial image. The proposed algorithm selected K neighboring face samples, which were more similar to the input face, from the training set, and then took them as reference examples after their registrations so as to learn the embedding coefficients of pixels in target HR image. The weights of respective face examples in the learning process were adaptively adjusted in order to reduce the influence of registration errors, and the total variation minimization was used to constrain the smoothness of embedding coefficients. Combining the local pixel embedding relationship and the degradation model, the method could reconstruct target HR image using the maximum posterior estimation framework. Experimental result shows that the reconstructed HR images can posses more delicate and clear local features, and the PSNR and SSIM are 1.26 dB and 0.04 higher than that of using comparison algorithms, respectively.