The face hallucinating two-step framework using hallucinated high-resolution residual
H. M. M. Naleer, Yao Lu, Yaozu An · Proceedings of SPIE, the International Society for Optical Engineering/Proceedings of SPIE · 2011
In video surveillance, the attention human faces are frequently of small size. Image hallucination is an imperative factor disturbing the face classification by human and computer. In this paper, we propose a two-step face hallucination framework by means of training data sets which have a small quantity of low and high resolution images. In the first step, the global face is constructed based on optimal weights of training images. In the second step, a local residual compensation method bases on position patch via residual training face image data sets. Moreover, the hallucinated highresolution residual image which is obtained by the identical process can be subsequent for the local face. Finally, the hallucinated high-resolution residual image is appended with the input low-resolution face image which is interpolated to the high-resolution image dimension by an upsampling factor. Experiments fully demonstrate that our framework is very flexible and accomplishs good performance via small training data sets.