Effective two-step method for face hallucination based on sparse compensation on over-complete patches

Sparse representation has been successfully applied to image d using low- and high-resolution training face images based on sparse representation. In this study, the sparse residual compensation is adopted to face hallucination. Firstly, a global face image is constructed by optimal coefficients of the interpolated training images. Secondly, the high-resolution residual image (local face image) is found by using an over-complete patch dictionary and the sparse representation. Finally, a hallucinated face image is obtained by combining these two steps. In addition, the more details of the face image in high frequency parts are recovered using a residual compensation strategy. In the authors´ experimental work, it is observed that balance sparsity parameter (λ) has affected the residual compensation. Further, the proposed algorithm can acquire a high-resolution image even though the number of training image pairs is comparatively smaller. The experiments show that the authors´ method is more effective than the other existing two-step face hallucination methods.