Face image super resolution via adaptive-block PCA

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.