Abstract
Single Image Super-Resolution (SISR) which aims to recover a high resolution (HR) image from a low-resolution (LR) image has a wide range of medical applications. In this paper, we present a novel Super-Resolution Coarse-to-Fine Network (SRCFN) that recovers the finer texture details strongly and enables precise high-frequency detail to address this challenging task. First, we apply some residuals units to achieve a coarse Super-Resolution result. Second, we add a fine module using the idea of segmentation networks to combine more high-frequency detail into the coarse results for final Super-Resolution results. In addition, we use a combined loss function of Mean square error loss and SSIM loss. Our proposed method applied to medical MRI outperforms previous methods of accuracy (PSNR and SSIM) and visual improvements.
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Acknowledgements
This work was supported in part by the National Natural Science Foundation of China (81771940, 81427803), National Key Research and Development Program of China (2017YFC0108000), Beijing Municipal Science & Technology Commission (Z151100003915079), and Beijing Municipal Natural Science Foundation (7172122).
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Liu, J., Chen, F., Shi, H., Liao, H. (2018). Single Image Super-Resolution for MRI Using a Coarse-to-Fine Network. In: Ibrahim, F., Usman, J., Ahmad, M., Hamzah, N., Teh, S. (eds) 2nd International Conference for Innovation in Biomedical Engineering and Life Sciences. ICIBEL 2017. IFMBE Proceedings, vol 67. Springer, Singapore. https://doi.org/10.1007/978-981-10-7554-4_42
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DOI: https://doi.org/10.1007/978-981-10-7554-4_42
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