A Review on Medical Image Super Resolution with Application of Deep Learning
DOI:
https://doi.org/10.24113/ijoscience.v7i2.368Keywords:
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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References
Zhengqiang Xiong, Manhui Lin “Single image super-resolution via Image Quality Assessment-Guided Deep Learning Network” PLoS One. 15(10): e0241313, 2020.
Ledig, C.; Theis, L.; Huszar, F.; Caballero, J.; Cunningham, A.; Acosta, A.; et al. Photo-Realistic Single Image Super-Resolution Using a Generative Adversarial Network. the IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2017.
Zhang K.; Tao D.; Gao X.; Li X.; Li J. Coarse-to-fine learning for single-image super-resolution. IEEE transactions on neural networks and learning systems 2016, 28, 1109–1122.
Yang, C.Y.; Ma, C.; Yang, M.H. Single-image super-resolution: A benchmark. European Conference on Computer Vision. Springer, 2014, pp. 372–386.
Zhang K.; Tao D.; Gao X.; Li X.; Xiong Z. Learning multiple linear mappings for efficient single image super-resolution. IEEE Transactions on Image Processing 2015, 24, 846–861. 10.1109/TIP.2015.2389629.
Ma C.; Yang C.Y.; Yang X.; Yang M.H. Learning a no-reference quality metric for single-image super-resolution. Computer Vision and Image Understanding 2017, 158, 1–16.
Yang W.; Zhang X.; Tian Y.; Wang W.; Xue J.H.; Liao Q. Deep learning for single image super-resolution: A brief review. IEEE Transactions on Multimedia 2019, 21, 3106–3121.
Tzu-An Song, Samadrita Roy Chowdhury , Fan Yang, and Joyita Dutta. 2020. Super-Resolution PET Imaging Using Convolutional Neural Networks. IEEE Transactions on Computational Imaging, 6, pp. 518-528
KlemanGrm and Walter J. Scheirer. 2020. Face Hallucination Using Cascaded Super-Resolution and Identity Priors. IEEE Transactions On Image Processing, 29.
Jun-Ho Choi et al. 2019. Deep Learning-based Image Super-Resolution Considering Quantitative and Perceptual Quality. Neuro Computing Elsevier.
Xunxiang Yao, QiangWu, Peng Zhang. 2019. Adaptive rational fractal interpolation function for image super-resolution via local fractal analysis. Image and Vision Computing82, pp. 39-49.
George Seif et al., 2018. Edge-based loss function for single image super-resolution. IEEE. International Conference on Acoustics, Speech and Signal Processing (ICASSP). pp. 1468-1472.
Y. LeCun, Y. Bengio, G. Hinton “Deep learning” Nature, 521 (2015), p. 436.
I. Goodfellow, Y. Bengio, A. Courville “Deep learning” MIT Press (2016)
Y. LeCun, L. Bottou, Y. Bengio, P. Haffner “Gradient-based learning applied to document recognition” Proc IEEE, 86 (1998), pp. 2278-2324
Nasrollahi K, Moeslund T. Super-resolution: a comprehensive survey. Mach Vis Appl 2014;25:1423–68.
Belekos S, Galatsanos N, Katsaggelos A. Maximum a posterio video superresolution using a new multichannel image prior. IEEE Trans Image Process 2010;19:1451–64.
Kawulok M, Benecki P, Piechaczek S, Hryczenko K, Kostrzewa D, Naleoa J. Deep learning for multiple-image super-resolution. Preprint arXiv:1903.00440, 2019.
Lim B, Son S, Kim H, Nah S, Lee KM. Enhanced deep residual networks for single image super-resolution. In: The IEEE conference on computer vision and pattern recognition (CVPR) workshops, vol. 1; 2017. p. 3.
Zhao H, Gallo O, Frosio I, Kautz J. Loss functions for neural networks for image processing. IEEE Trans Comput Imaging 2017;3.
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Copyright (c) 2021 Kajol Singh, Manish Saxena
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