| Author: | Liu, Ying |
| Title: | Human face super-resolution |
| Degree: | M.Sc. |
| Year: | 2012 |
| Subject: | High resolution imaging. Image processing -- Digital techniques. Image processing -- Mathematical models. Hong Kong Polytechnic University -- Dissertations |
| Department: | Department of Electronic and Information Engineering |
| Pages: | 98 p. : ill. ; 30 cm. |
| Language: | English |
| Abstract: | In this thesis, we have investigated different algorithms for human face super-resolution (SR), which are important for applications such as face recognition, video surveillance and application of many digital devices etc. With these face SR algorithms, face-image resolution can be increased while the facial-image quality is maintained. We have studied two types of SR algorithms: reconstruction-based and learning-based methods. For reconstruction-based methods, we have investigated and implemented the "bilinear" method and the "bicubic" method. These methods are simple, but can achieve only a limited performance, since limitation of information provided. In order to achieve a better performance, learning-based methods are usually employed; these learn the relations between low-resolution (LR) and high-resolution (HR) images from a dataset containing pairs of LR-HR pairs. We have investigated and implemented the "eigentransformation" method, which use principal component analysis (PCA) to represent a face image as a linear combination of training samples. We have proposed two improvements to this method. The first improvement is that, instead of considering the linear relations between a LR face image and the LR training samples, LR images are first super-resolved using a reconstruction-based method, and then the linear relations are computed. The other improvement is to use a face-recognition method to search similar faces to an input LR face before eigentransformation is applied. We also compare the eigentransformation methods to a patch-based method, namely position patch. We evaluate the respective performances of the different algorithms in terms of visual quality and some other objective measurements. |
| Rights: | All rights reserved |
| Access: | restricted access |
Files in This Item:
| File | Description | Size | Format | |
|---|---|---|---|---|
| b25075494.pdf | For All Users (off-campus access for PolyU Staff & Students only) | 3.34 MB | Adobe PDF | View/Open |
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