A Review on Medical Image Super Resolution with Application of Deep Learning

Authors

  • Kajol Singh M Tech Scholar, Bansal Institute of Science and Technology, Bhopal (M.P), India
  • Manish Saxena Professor, Bansal Institute of Science and Technology, Bhopal (M.P), India

DOI:

https://doi.org/10.24113/ijoscience.v7i2.368

Keywords:

LR, HR, CNN, DNN.

Abstract

Super resolution problems are often discussed in medical imaging. The spatial resolution of medical images is insufficient due to limitations such as image acquisition time, low radiation dose or hardware limitations. Various super-resolution methods have been proposed to solve these problems, such as optimization or learning-based approaches. Recently, deep learning methodologies have become a thriving technology and are evolving at an exponential rate. We believe we need to write a review to illustrate the current state of deep learning in super-resolution medical imaging. In this article, we provide an overview of image resolution and the deep learning introduced in super resolution. This document describes super resolution for single images versus super resolution for multiple images, evaluation metrics and loss functions.

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Published

03/27/2021

How to Cite

Singh , K. ., & Saxena, M. . (2021). A Review on Medical Image Super Resolution with Application of Deep Learning. SMART MOVES JOURNAL IJOSCIENCE, 7(2), 25–29. https://doi.org/10.24113/ijoscience.v7i2.368

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