Single image super-resolution of medical ultrasound images using a fast algorithm

Abstract : This paper addresses the problem of super-resolution (SR) for medical ultrasound (US) images. Contrary to device-based approaches, we investigate a post-processing method to invert the direct linear model of US image formation. Given the ill-posedness of single image SR, we proposed an ℓp-norm (1 ≤ p ≤ 2) regularizer for the US tissue reflectivity function/image to be estimated. To solve the associated optimization problem, we propose a novel way to explore the decimation and blurring operators simultaneously. As a consequence, we are able to compute the analytical solution for the ℓ2-norm regularized SR problem and to embed the analytical solution to an alternating direction method of multipliers for the ℓp-norm regularized SR problem. The behavior of the proposed algorithm is illustrated using synthetic, simulated and in vivo US data.
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Ningning Zhao, Qi Wei, Adrian Basarab, Denis Kouamé, Jean-Yves Tourneret. Single image super-resolution of medical ultrasound images using a fast algorithm . 13th IEEE International Symposium on Biomedical Imaging: From Nano to Macro (ISBI 2016), Apr 2016, Prague, Czech Republic. pp.473-476, ⟨10.1109/ISBI.2016.7493310⟩. ⟨hal-01566911⟩

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