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Super-Resolution in Medical Imaging

Published: 01 January 2009 Publication History

Abstract

This paper provides an overview on super-resolution (SR) research in medical imaging applications. Many imaging modalities exist. Some provide anatomical information and reveal information about the structure of the human body, and others provide functional information, locations of activity for specific activities and specified tasks. Each imaging system has a characteristic resolution, which is determined based on physical constraints of the system detectors that are in turn tuned to signal-to-noise and timing considerations. A common goal across systems is to increase the resolution, and as much as possible achieve true isotropic 3-D imaging. SR technology can serve to advance this goal. Research on SR in key medical imaging modalities, including MRI, fMRI and PET, has started to emerge in recent years and is reviewed herein. The algorithms used are mostly based on standard SR algorithms. Results demonstrate the potential in introducing SR techniques into practical medical applications.

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  • (2025)Dual stage semantic information based generative adversarial network for image super-resolutionComputer Vision and Image Understanding10.1016/j.cviu.2024.104226250:COnline publication date: 1-Jan-2025
  • (2025)Reference-based image super-resolution with attention extraction and pooling of residualsThe Journal of Supercomputing10.1007/s11227-024-06587-881:1Online publication date: 1-Jan-2025
  • (2024)A High-Performance Accelerator for Real-Time Super-Resolution on Edge FPGAsACM Transactions on Design Automation of Electronic Systems10.1145/365285529:3(1-25)Online publication date: 16-Mar-2024
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Published In

cover image The Computer Journal
The Computer Journal  Volume 52, Issue 1
January 2009
167 pages

Publisher

Oxford University Press, Inc.

United States

Publication History

Published: 01 January 2009

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Cited By

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  • (2025)Dual stage semantic information based generative adversarial network for image super-resolutionComputer Vision and Image Understanding10.1016/j.cviu.2024.104226250:COnline publication date: 1-Jan-2025
  • (2025)Reference-based image super-resolution with attention extraction and pooling of residualsThe Journal of Supercomputing10.1007/s11227-024-06587-881:1Online publication date: 1-Jan-2025
  • (2024)A High-Performance Accelerator for Real-Time Super-Resolution on Edge FPGAsACM Transactions on Design Automation of Electronic Systems10.1145/365285529:3(1-25)Online publication date: 16-Mar-2024
  • (2024)Toward DNN of LUTs: Learning Efficient Image Restoration With Multiple Look-Up TablesIEEE Transactions on Pattern Analysis and Machine Intelligence10.1109/TPAMI.2024.340104846:12(8284-8301)Online publication date: 1-Dec-2024
  • (2024)Uncovering the Over-Smoothing Challenge in Image Super-Resolution: Entropy-Based Quantification and Contrastive OptimizationIEEE Transactions on Pattern Analysis and Machine Intelligence10.1109/TPAMI.2024.337870446:9(6199-6215)Online publication date: 1-Sep-2024
  • (2024)RISTRA: Recursive Image Super-Resolution Transformer With Relativistic AssessmentIEEE Transactions on Multimedia10.1109/TMM.2024.335240026(6475-6487)Online publication date: 10-Jan-2024
  • (2024)Frequency Generation for Real-World Image Super-ResolutionIEEE Transactions on Circuits and Systems for Video Technology10.1109/TCSVT.2024.336787634:8(7029-7040)Online publication date: 1-Aug-2024
  • (2024)Medical image super-resolution for smart healthcare applicationsInformation Fusion10.1016/j.inffus.2023.102075103:COnline publication date: 1-Mar-2024
  • (2024)MR image reconstruction using iterative up and downsampling networkExpert Systems with Applications: An International Journal10.1016/j.eswa.2023.121590237:PCOnline publication date: 1-Mar-2024
  • (2024)Image super-resolution reconstruction using Swin Transformer with efficient channel attention networksEngineering Applications of Artificial Intelligence10.1016/j.engappai.2024.108859136:PBOnline publication date: 18-Nov-2024
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