Face Hallucination in a high resolution feature space using an intermediate dictionary learning via reference patch embedding

In this paper, ambiguity in low resolution U+0028 LR U+0029 and high resolution U+0028 HR U+0029 manifold for nearest neighbor selection in face hallucination U+0028 FH U+0029 problem is considered. To highlight the performance of FH we propose to resolve the ambiguity through two measures. Firstly, an improved search criterion, i.e., reference patch embedding U+0028 RPE U+0029 is designed for neighbor embedding U+0028 NE U+0029 to enhance the structural similarity in LR manifold. Secondly, locality constrained partial least square U+0028 PLS U+0029 estimation is employed for NE in HR manifold. PLS maximizes the degree of similarity between two manifolds and share almost the same local structure. Therefore locality constrained refined neighbor selection in a unified feature space better optimizes the reconstruction weights, thus the performance is improved. It is illustrated with the help of extensive experiments that proposed methods leads to better performance with respect to peak signal to noise ratio U+0028 PSNR U+0029 and structural similarity index matrix U+0028 SSIM U+0029 as compared to the results obtained by traditional position patch based methods for FH.