Hallucinating Faces: Global Linear Modal Based Super-Resolution and Position Based Residue Compensation

A learning-based face hallucination method is proposed in this paper for the reconstruction of a high-resolution face image from a low-resolution observation based on a set of high- and low-resolution training image pairs. The proposed global linear modal based super-resolution estimates the optimal weights of all the low-resolution training images and a high-resolution image is obtained by applying the estimated weights to the high-resolution space. Then, we propose a position based local residue compensation algorithm to better recover subtle details of face. Experiments demonstrate that our method has advantage over some established methods.

• Type: Conference
• Year: 2009
• Where: ICIAP
• Authors: Xiang Ma, Junping Zhang, Chun Qi
• Keywords: Face Hallucination Method, ICIAP 2009, Image Processing, Low-resolution Observation, Low-resolution Training Images