Single face image super-resolution via solo dictionary learning
In this work, we have proposed a single face image super-resolution approach based on solo dictionary learning. The core idea of the proposed method is to recast the super-resolution task as a missing pixel problem, where the low-resolution image is considered as its high-resolution counterpart with many pixels missing in a structured manner. A single dictionary is therefore sufficient for recovering the super-resolved image by filling the missing pixels. In order to fill in 93.75% of the missing pixels when super-resolving a 16 × 16 low-resolution image to a 64 × 64 one, we adopt a whole image-based solo dictionary learning scheme. The proposed procedure can be easily extended to low-resolution input images with arbitrary dimensions, as well as high-resolution recovery images of arbitrary dimensions. Also, for a fixed desired super-resolution dimension, there is no need to retrain the dictionary when the input low-resolution image has arbitrary zooming factors. Based on a large-scale fidelity experiment on the FRGC ver2 database, our proposed method has outperformed other well established interpolation methods as well as the coupled dictionary learning approach.