Abstract
The paper proposes a novel super-resolution reconstruction algorithm for human faces. The algorithm extracts training examples from the input image and divides them into several classes using vector quantization. Then, it classifies each patch from a low-resolution image as one of these classes. Each class has its high-frequency information inferred using a parallel designed multi-class predictor, which is trained using the training samples from the same class. The self-example training set and the specific domain training set were employed in investigation of the impact of the training database. The experimental results showed the superior performance of the proposed method in terms of both the reconstruction quality and runtime.
Original language | English |
---|---|
Pages (from-to) | 377-381 |
Number of pages | 5 |
Journal | Gaojishu Tongxin/Chinese High Technology Letters |
Volume | 19 |
Issue number | 4 |
Publication status | Published - 1 Apr 2009 |
Keywords
- Example-based learning
- Human face magnification
- Multi-class predictor
- Self-example
- Super-resolution restoration
ASJC Scopus subject areas
- General Engineering