Super-resolution facial images from single input images based on discrete wavelet transform
2014 (English)In: Proceedings - International Conference on Pattern Recognition, 2014, 843-848 p.Conference paper (Refereed)
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.
Place, publisher, year, edition, pages
2014. 843-848 p.
Discrete Wavelet Transform, Image generation, Principal Component Analysis, Super Resolution, Discrete wavelet transforms, Frequency estimation, Image enhancement, Image resolution, Optical resolving power, Pattern recognition, Wavelet transforms, Accurate estimation, Frequency contents, Higher dimensions, Image generations, Low qualities, Original images, Reliable methods
Computer and Information Science
IdentifiersURN: urn:nbn:se:kth:diva-168871DOI: 10.1109/ICPR.2014.155ISI: 000359818000143ScopusID: 2-s2.0-84919934817ISBN: 9781479952083OAI: oai:DiVA.org:kth-168871DiVA: diva2:819514
22nd International Conference on Pattern Recognition, ICPR 2014, 24 August 2014 through 28 August 2014
QC 201506102015-06-102015-06-092015-09-14Bibliographically approved