Face Super-resolution based-on Non-negative Matrix Factorization
Tao Lu, Ruimin Hu, Chengdong Lan, Zhen Han · International Journal of Digital Content Technology and its Applications · 2011
Principal Component Analysis (PCA) is a classical method which is commonly used for human face images representation in face super-resolution. But the features extracted by PCA are holistic and difficult to have semantic interpretation. In order to synthesize a high-resolution face image with structural details, we propose a face super-resolution algorithm based on non-negative matrix factorization (NMF). This algorithm uses the NMF to obtain structural information representation of face images, and then the target image is regularized by Markov random fields, with maximum a posteriori probability approach. Finally, the steepest descent method is used to optimize NMF coefficient of high-resolution image. Experiment results demonstrate that the NMF-based face super-resolution algorithm performs better than PCA-based algorithms, in the subjective and objective quality.