Magnetic Resonance Imaging (MRI) is an important area in medical enviroment. Many studies base their diagnosis upon what it seems in the images. However, there are critical issues while dealing with MRI; noise sensibility, long acquisition sessions, appearance of artifacts, blurring, etc. One way to counter measure blurring and long sessions is by using Super-Resolution (SR) algorithms. In this work, we present three approachs to evaluate the selected SR algorithms in MRI: 1) Enhance images’ resolution from whole studies with three classical SR algorithms, 2) use quantitative metrics to evaluate the chosen algorithms, 3) do a measure of processing time through computer’s CPU. Our results determined which algorithm has better performance at processing MRI in terms of image quality and processing time.


    Access

    Check access

    Check availability in my library

    Order at Subito €


    Export, share and cite



    Title :

    Evaluation of Classic Super-Resolution Algorithms for Magnetic Resonance Images


    Contributors:


    Publication date :

    2017-11-01


    Size :

    2091494 byte




    Type of media :

    Conference paper


    Type of material :

    Electronic Resource


    Language :

    English



    Quantum Images with Super-Resolution

    Balakin, D. / Belinsky, A. | British Library Online Contents | 2015


    Generalized multi-resolution analysis of magnetic resonance images

    Spaniol, J. / Belmont, M. R. / Summers, I. R. et al. | British Library Online Contents | 1994


    Super-Resolution from Highly Undersampled Images

    Vandewalle, P. / Sbaiz, L. / Vetterli, M. et al. | British Library Conference Proceedings | 2005


    Super-resolution from highly undersampled images

    Vandewalle, P. / Sbaiz, L. / Vetterli, M. et al. | IEEE | 2005


    Super-Resolution Image Restoration from Blurred Low-Resolution Images

    Ng, M. K. / Yau, A. C. | British Library Online Contents | 2005