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Advances in single frame image recovery

Ali Pitchay, Sakinah (2013)
Ph.D. thesis, University of Birmingham.

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Abstract

This thesis tackles a problem of recovering a high resolution image from a single compressed frame. A new image-prior that is devised based on Pearson type VII density is integrated with a Markov Random Field model which has desirable robustness properties. A fully automated hyper-parameter estimation procedure for this approach is developed, which makes it advantageous in comparison with alternatives. Although this recovery algorithm is very simple to implement, it achieves statistically significant improvements over previous results in under-determined problem settings, and it is able to recover images that contain texture.

This advancement opens up the opportunities for several potential extensions, of which we pursue two:
(i) Most of previous work does not consider any specific extra information to recover the signal. Thus, this thesis exploits the similarity between the signal of interest and a consecutive motionless frame to address this problem. Additional information of similarity that is available is incorporated into a probabilistic image-prior based on the
Pearson type VII Markov Random Field model. Results on both synthetic and real data of
Magnetic Resonance Imaging (MRI) images demonstrate the effectiveness of our method in both compressed setting and classical super-resolution experiments.
(ii) This thesis also presents a multi-task approach for signal recovery by sharing higher-level hyperparameters which do not relate directly to the actual content of the signals of interest but only to their statistical characteristics. Our approach leads to a very simple model and algorithm that can be used to simultaneously recover multiple

Type of Work:Ph.D. thesis.
Supervisor(s):Kab´an, Ata
School/Faculty:Colleges (2008 onwards) > College of Engineering & Physical Sciences
Department:School of Computer Science
Subjects:T Technology (General)
TR Photography
Institution:University of Birmingham
ID Code:4292
This unpublished thesis/dissertation is copyright of the author and/or third parties. The intellectual property rights of the author or third parties in respect of this work are as defined by The Copyright Designs and Patents Act 1988 or as modified by any successor legislation. Any use made of information contained in this thesis/dissertation must be in accordance with that legislation and must be properly acknowledged. Further distribution or reproduction in any format is prohibited without the permission of the copyright holder.
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