Author

Chen Chen

Advisor

Fowler, James E.

Committee Member

Younan, Nicolas H.

Committee Member

Moorhead, Robert J.

Date of Degree

1-1-2012

Document Type

Graduate Thesis - Open Access

Major

Electrical and Computer Engineering

Degree Name

Master of Science

College

James Worth Bagley College of Engineering

Department

Department of Electrical and Computer Engineering

Abstract

A process for the use of multihypothesis prediction in the reconstruction of images is proposed for use in both compressed-sensing reconstruction as well as single-image super-resolution. Specifically, for compressed-sensing reconstruction of a single still image, multiple predictions for an image block are drawn from spatially surrounding blocks within an initial non-predicted reconstruction. The predictions are used to generate a residual in the domain of the compressed-sensing random projections. This residual being typically more compressible than the original signal leads to improved compressed-sensing reconstruction quality. To appropriately weight the hypothesis predictions, a Tikhonov regularization to an ill-posed least-squares optimization is proposed. An extension of this framework is applied to the compressed-sensing reconstruction of hyperspectral imagery is also studied. Finally, the multihypothesis paradigm is employed for single-image superresolution wherein each patch of a low-resolution image is represented as a linear combination of spatially surrounding hypothesis patches.

URI

https://hdl.handle.net/11668/17679

Comments

Compressed Sensing||Tikhonov Regularization||Multihypothesis Prediction||Image Super-resolution||Hyperspectral Data

Share

COinS