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Optimization Methods for Medical Image Super Resolution Reconstruction

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Applications of Intelligent Optimization in Biology and Medicine

Part of the book series: Intelligent Systems Reference Library ((ISRL,volume 96))

Abstract

Super-resolution (SR) concentrates on constructing a high-resolution (HR) image of a scene from two or more sets of low-resolution (LR) images of the same scene. It is the process of combining a sequence of low-resolution (LR) noisy blurred images to produce a higher-resolution image. The reconstruction of high-resolution images is computationally expensive. SR is defined to be an inverse problem that is well-known as ill-conditioned. The SR problem has been reformulated using optimization techniques to define a solution that is a close approximation of the true scene and less sensitive to errors in the observed images. This paper reviews the optimized SR reconstruction approaches and highlights its challenges and limitations. An experiment has been done to compare between bicubic, iterative back-projection (IBP), projected onto convex sets (POCS), total variation (TV) and Gradient descent via sparse representation. The experimental results show that Gradient descent via sparse representation outperforms other optimization techniques.

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Authors and Affiliations

  1. Data Reception, Analysis and Receiving Station Affairs, National Authority for Remote, Sensing and Space Science, Cairo, Egypt

    Marwa Moustafa & Ashraf Helmy

  2. Faculty of Computer and Information, Sciences, Ain Shams University, Cairo, Egypt

    Hala M. Ebied, Taymoor M. Nazamy & Mohamed F. Tolba

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  1. Marwa Moustafa
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  2. Hala M. Ebied
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  3. Ashraf Helmy
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  4. Taymoor M. Nazamy
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  5. Mohamed F. Tolba
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Correspondence to Mohamed F. Tolba .

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  1. Information Technology Department, Cairo University, Giza, Egypt

    Aboul-Ella Hassanien

  2. Department of Information Systems and Computing, Brunel University, London, United Kingdom

    Crina Grosan

  3. Faculty of Computers and information, Ain Shams University, Cairo, Egypt

    Mohamed Fahmy Tolba

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Moustafa, M., Ebied, H.M., Helmy, A., Nazamy, T.M., Tolba, M.F. (2016). Optimization Methods for Medical Image Super Resolution Reconstruction. In: Hassanien, AE., Grosan, C., Fahmy Tolba, M. (eds) Applications of Intelligent Optimization in Biology and Medicine. Intelligent Systems Reference Library, vol 96. Springer, Cham. https://doi.org/10.1007/978-3-319-21212-8_6

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