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 ca...

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Bibliographic Details
Main Authors: Darvish, Ali Mohammed, Haibo Li, Soderstrom, Ulrik
Format: Conference Proceeding
Language:English
Subjects:
Discrete Wavelet Transform
Discrete wavelet transforms
Frequency estimation
Image coding
Image generation
Image reconstruction
Image resolution
Principal Component Analysis
PSNR
Super Resolution
Video sequences
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Description
Summary: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.
ISSN:1051-4651
2831-7475
DOI:10.1109/ICPR.2014.155