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Abstract #3412

A novel non convex sparse recovery method for single image super-resolution, denoising and iterative MR reconstruction

Nishant Zachariah 1 , Johannes M Flake 2 , Qiu Wang 3 , Boris Mailhe 3 , Justin Romberg 1 , Xiaoping Hu 4 , and Mariappan Nadar 3

1 Department of Electrical and Computer Engineering, Georgia Institute of Technoloy, Atlanta, GA, United States, 2 Department of Mathematics, Rutgers University, New Brunswick, NJ, United States, 3 Imaging and Computer Vision, Siemens Corporate Technology, Princeton, NJ, United States, 4 Department of Biomedical Engineering, Emory University and Georgia Institute of Technology, Atlanta, GA, United States

Increasing MR image resolution, decreasing MR instrumentation noise and reconstructing high quality MR images from under sampled measurements are open challenges. In this paper we tackle these three problems under a novel non convex framework. We show that our method out performs state of the art techniques (quantitatively and qualitatively) for image super-resolution, denoising and under sampled reconstruction. In addition, we are able to recover regions of clinical interest with greatest fidelity thereby substantially aiding the clinical diagnostic process. Our powerful generic framework lends itself to tackling additional future applications such as image in-painting and blind de-convolution.

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