Super-resolution Facial Images from Single Input Images Based on Discrete Wavelet Transform

In this work, we are presenting a technique that allows for accurate estimation of frequencies in higher dimensions than the original image content. This technique uses asymmetrical Principal Component Analysis together with Discrete Wavelet Transform (aPCA-DWT). For example, high quality content can be generated from low quality cameras since the necessary frequencies can be estimated through reliable methods. Within our research, we build models for interpreting facial images where super-resolution versions of human faces can be created. We have worked on several different experiments, extracting the frequency content in order to create models with aPCA-DWT. The results are presented along with experiments of deblurring and zooming beyond the original image resolution. For example, when an image is enlarged 16 times in decoding, the proposed technique outperforms interpolation with more than 7 dB on average.