Yu, M., Xu, Z., & Lukasiewicz, T. (2025). A general survey on medical image super-resolution via deep learning. Computers in Biology and Medicine, 193, Article 110345. https://doi.org/10.1016/j.compbiomed.2025.110345
E192-07 - Forschungsbereich Artificial Intelligence Techniques E192-03 - Forschungsbereich Knowledge Based Systems
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Journal:
Computers in Biology and Medicine
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ISSN:
0010-4825
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Date (published):
Jul-2025
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Number of Pages:
18
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Publisher:
PERGAMON-ELSEVIER SCIENCE LTD
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Peer reviewed:
Yes
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Keywords:
Humans; Surveys and Questionnaires; Deep learning; Effective architecture; Low-level vision; Medical image super-resolution; Spatial resolution; Deep Learning; Image Processing, Computer-Assisted; Diagnostic Imaging
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Abstract:
Medical image super-resolution (SR) is a classic regression task in low-level vision. Limited by hardware limitations, acquisition time, low radiation dose, and other factors, the spatial resolution of some medical images is not sufficient. To address this problem, many different SR methods have been proposed. Especially in recent years, medical image SR networks based on deep learning have been vigorously developed. This survey provides a modular and detailed introduction to the key components of medical image SR technology based on deep learning. In this paper, we first introduce the background concepts of deep learning and medical image SR task. Subsequently, we present a comprehensive analysis of the key components from the perspectives of effective architecture, upsampling module, learning strategy, and image quality assessment of medical image SR networks. Furthermore, we focus on the urgent problems that need to be addressed in the medical image SR task based on deep learning. And finally we summarize the trends and challenges of future development.
