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A deblurring model for super-resolution MRI interpolated images
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Paper Abstract

In the up-sampling process may occur effects like aliasing, blurring or noise addition which mainly affect the edges of the images. For those reasons is necessary to choose a method that preserves images quality so that these problems are minimized. In this paper, we present an alternative method to restore blurred images using linear programming to solve a minimization problem stated in the L1 norm. The model requires the blurred image and some prior knowledge about the blurring function type (Point spread function). In the proposed method we obtain a PSNR of 30 dB overcoming a classic bi-linear method by 4 dB in a set of thirty images from a cardiac MRI data set.

Paper Details

Date Published: 3 January 2020
PDF: 9 pages
Proc. SPIE 11330, 15th International Symposium on Medical Information Processing and Analysis, 113300Q (3 January 2020); doi: 10.1117/12.2542584
Show Author Affiliations
José Fuentes, Univ. Nacional de Colombia (Colombia)
Jorge Mauricio Ruiz V, Univ. Nacional de Colombia (Colombia)


Published in SPIE Proceedings Vol. 11330:
15th International Symposium on Medical Information Processing and Analysis
Eduardo Romero; Natasha Lepore; Jorge Brieva, Editor(s)

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