Super-resolution Reconstruction for Facial Images Based on Local Principal Component Analysis
Jin ping He, Guang da Su, Jian sheng Chen
Jin ping He
Available Online August 2013.
- https://doi.org/10.2991/icacsei.2013.62How to use a DOI?
- Super-resolution reconstruction, Hallucinating faces, Local Principal Component Analysis.
- In order to improve the ghosting effect appearing the reconstruction results which are obtained through applying Principle Component Analysis (PCA) on the whole images, a novel algorithm which is reconstructed through applying PCA on local image patches is proposed. The new method firstly proposes the image patch model with overlapping areas. Then the input low-resolution patches are projected on the sample patches through PCA. And the weights can be obtained. Furthermore, the corresponding high-resolution patches are linearly combined through these weights to output the fusion patches. Now the best results are 16×12 reconstructions with the magnification of 8×8. Experimentations show that our method can reconstruct ultra-low resolution faces of 8×6 pixels with the magnification of 16×16, and the similarity with the original high-resolution images is higher.
- Open Access
- This is an open access article distributed under the CC BY-NC license.
Cite this article
TY - CONF AU - Jin ping He AU - Guang da Su AU - Jian sheng Chen PY - 2013/08 DA - 2013/08 TI - Super-resolution Reconstruction for Facial Images Based on Local Principal Component Analysis BT - 2013 International Conference on Advanced Computer Science and Electronics Information (ICACSEI 2013) PB - Atlantis Press SN - 1951-6851 UR - https://doi.org/10.2991/icacsei.2013.62 DO - https://doi.org/10.2991/icacsei.2013.62 ID - He2013/08 ER -