Performance Evaluation of the Nearest Feature Line Method in Image Classification and Retrieval
IEEE Transactions on Pattern Analysis and Machine Intelligence
International Journal of Computer Vision - Special issue on statistical and computational theories of vision: modeling, learning, sampling and computing, Part I
Limits on Super-Resolution and How to Break Them
IEEE Transactions on Pattern Analysis and Machine Intelligence
Face Hallucination: Theory and Practice
International Journal of Computer Vision
Hallucinating face by position-patch
Pattern Recognition
Super-resolution of human face image using canonical correlation analysis
Pattern Recognition
Hallucinating face by eigentransformation
IEEE Transactions on Systems, Man, and Cybernetics, Part C: Applications and Reviews
The CAS-PEAL Large-Scale Chinese Face Database and Baseline Evaluations
IEEE Transactions on Systems, Man, and Cybernetics, Part A: Systems and Humans
| Hi-index | 0.00 |
In this paper, we propose a manifold learning based algorithm using 'Nearest Feature Line - NFL' to hallucinate high-resolution face image. According to the fact that existing NFL can effectively characterize the geometrical proportions to the face samples, we propose using NFL metric to define the neighborhood relations between face samples. Our algorithm can solve the problem that traditional method cannot effectively reveal the similar local geometry between high-resolution and low-resolution face manifolds under the condition that the training sample size is small. Moreover, in order to enhance the representation capacity of available face samples and reduce the computational complexity, we select neighborhood samples for each input LR image. Experimental results demonstrate that our algorithm can generates clearer local feature details, and the PSNR is 1.4 dB higher than that of the best manifold learning based method reported so far.