title: Alignment-free and high-frequency compensation in face hallucination.
abstract: Face hallucination is one of learning-based super resolution techniques, which is focused on resolution enhancement of facial images. Though face hallucination is a powerful and useful technique, some detailed high-frequency components cannot be recovered. It also needs accurate alignment between training samples. In this paper, we propose a high-frequency compensation framework based on residual images for face hallucination method in order to improve the reconstruction performance. The basic idea of proposed framework is to reconstruct or estimate a residual image, which can be used to compensate the high-frequency components of the reconstructed high-resolution image. Three approaches based on our proposed framework are proposed. We also propose a patch-based alignment-free face hallucination. In the patch-based face hallucination, we first segment facial images into overlapping patches and construct training patch pairs. For an input low-resolution (LR) image, the overlapping patches are also used to obtain the corresponding high-resolution (HR) patches by face hallucination. The whole HR image can then be reconstructed by combining all of the HR patches. Experimental results show that the high-resolution images obtained using our proposed approaches can improve the quality of those obtained by conventional face hallucination method even if the training data set is unaligned.
authors.affiliation: College of Computer Science and Information Technology, Central South University of Forestry and Technology, Hunan 410004, China ; College of Information Science and Engineering, Ritsumeikan University, Shiga 525-8577, Japan.
authors.forename: Yen-Wei
authors.initials: YW
authors.lastname: Chen
authors.affiliation: College of Information Science and Engineering, Ritsumeikan University, Shiga 525-8577, Japan.
authors.forename: So
authors.identifier: 0000-0002-2635-9105
authors.initials: S
authors.lastname: Sasatani
authors.affiliation: College of Information Science and Engineering, Ritsumeikan University, Shiga 525-8577, Japan.
authors.forename: Xian-Hua
authors.initials: XH
authors.lastname: Han
mesh_terms: D000465:Algorithms; D056667:Biometric Identification / Q000379:methods*; D003196:Computer Graphics; D005145:Face / Q000033:anatomy & histology*; D006212:Hallucinations; D006801:Humans