Multihypothesis Prediction for Compressed Sensing and Super-resolution of Images

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. Experimental results demonstrate that the proposed reconstruction significantly outperforms alternative compressed-sensing strategies not employing multihy-pothesis prediction. Finally, the multihypothesis paradigm is employed for single-image super-resolution wherein each patch of a low-resolution image is represented as a linear combination of spatially surrounding hypothesis patches. The coefficients of this representation are calculated using Tikhonov regularization and then used to generate a high-resolution image. ACKNOWLEDGMENTS I would like to express a sincere gratitude to my mentor and advisor Dr. James E. Fowler for his advice and support while working on this research. He has introduced me to this exciting field of compressed sensing and given me insights into a broad range of research topics. I sincerely thank my committee members, Dr. of my questions about regularization. In addition, I thank all of my current lab mates, Eric help and discussions during my research. Finally, I want to thank my family for always being there, and my parents and my wife for their love and understanding.