The face hallucinating two-step framework using hallucinated high-resolution residual

Abstract

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.

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

“…As stated by Liu et al [17], most of the existing researches model faces hallucination as two-step problem [18][19][20][21][22][23]. Firstly, a global face image is generated, which holds the main characteristics of the original high-resolution face but lacks some comprehensive features.…”