Improving resolution by image registration
CVGIP: Graphical Models and Image Processing
Example-Based Super-Resolution
IEEE Computer Graphics and Applications
FG '00 Proceedings of the Fourth IEEE International Conference on Automatic Face and Gesture Recognition 2000
An improved super-resolution with manifold learning and histogram matching
ICB'06 Proceedings of the 2006 international conference on Advances in Biometrics
Hallucinating face by eigentransformation
IEEE Transactions on Systems, Man, and Cybernetics, Part C: Applications and Reviews
Extraction of high-resolution frames from video sequences
IEEE Transactions on Image Processing
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In this paper, based on Circularly Symmetrical Gabor Transform (CSGT) and Principal Component Analysis (PCA), we propose a face hallucination approach. In this approach, all of the face images (both input face image and original training database) are transformed through CSGT at first and then local extremes criteria is utilized to extract the intrinsic features of the faces. Based on these features, we calculate Euclidean distances between the input face image and every face image in the original training database, and then Euclidean distances are used as criteria to choose the reasonable training database. Once the training database is chosen, PCA is applied to hallucinate the input face image as the linear combination of the chosen training images. Experimental results show that our approach can choose training database automatically according to the input face image and get high quality super-resolution image.