Face Image Super
Resolution via Adaptive-Block PCA
Lin Cao and Dan Liu
Department of
Telecommunication Engineering, Beijing Information Science
and
Technology University, China
Abstract: A novel single face image Super Resolution (SR) framework
based on adaptive-block Principal
Component Analysis (PCA)
is presented in this paper. The basic idea is the reconstruction of a High Resolution (HR) face image from a Low Resolution (LR)
observation based on a set of HR and LR training image pairs. The HR image block is generated in
the proposed method by using
the same position image blocks of each training image. The test face image and the training image
sets are divided into
many overlapping blocks, then these image blocks are classified according to
the characteristics of the image block and then PCA is operated directly on the non-flat image blocks to extract the optimal weights and the hallucinated patches are reconstructed
using the same weights. The final HR facial image is formed by integrating the
hallucinated patches. Experiments indicate that the new method produces HR faces
of higher quality and costs less computational time than some recent face image
SR techniques.
Keywords: SR, face image, adaptive-block,
PCA.
Received October 30, 2013; accepted November 20, 2014; Published online December 23, 2015