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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