Effective two‐step method for face hallucination based on sparse compensation on over‐complete patches
Mohamed Naleer Haju Mohamed, Yao Lu, Feng Lv · IET Image Processing · 2013
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.