Example-Based Super-Resolution
IEEE Computer Graphics and Applications
Comprehensive Database for Facial Expression Analysis
FG '00 Proceedings of the Fourth IEEE International Conference on Automatic Face and Gesture Recognition 2000
The CMU Pose, Illumination, and Expression (PIE) Database
FGR '02 Proceedings of the Fifth IEEE International Conference on Automatic Face and Gesture Recognition
SSPR & SPR '08 Proceedings of the 2008 Joint IAPR International Workshop on Structural, Syntactic, and Statistical Pattern Recognition
Joint Blind Super-Resolution and Shadow Removing
IEICE - Transactions on Information and Systems
An Example-Based Two-Step Face Hallucination Method through Coefficient Learning
ICIAR '09 Proceedings of the 6th International Conference on Image Analysis and Recognition
Face hallucination and recognition
AVBPA'03 Proceedings of the 4th international conference on Audio- and video-based biometric person authentication
Hi-index | 0.00 |
Recently a growing interest has been seen in single-frame super-resolution techniques, which are known as example-based or learning based super-resolution techniques. Face Hallucination is one of such 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. 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. 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.