Fowler, James E.
Younan, Nicolas H.
Moorhead, Robert J.
Date of Degree
Graduate Thesis - Open Access
Electrical and Computer Engineering
Master of Science
James Worth Bagley College of Engineering
Department of Electrical and Computer Engineering
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.
Chen, Chen, "Multihypothesis Prediction for Compressed Sensing and Super-Resolution of Images" (2012). Theses and Dissertations MSU. 3226.