A deblurring model for super-resolution MRI interpolated images

José Fuentes; Jorge Mauricio Ruiz V

doi:10.1117/12.2542584

3 January 2020 A deblurring model for super-resolution MRI interpolated images

Proceedings Volume 11330, 15th International Symposium on Medical Information Processing and Analysis; 113300Q (2020) https://doi.org/10.1117/12.2542584
Event: 15th International Symposium on Medical Information Processing and Analysis, 2019, Medelin, Colombia

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.

Citation Download Citation

José Fuentes and Jorge Mauricio Ruiz V "A deblurring model for super-resolution MRI interpolated images", Proc. SPIE 11330, 15th International Symposium on Medical Information Processing and Analysis, 113300Q (3 January 2020); https://doi.org/10.1117/12.2542584

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