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2011, Digital imaging and computer vision, ISBN 1439819300, xvii, 472
Book
Magnetic Resonance in Medicine, ISSN 0740-3194, 01/2018, Volume 79, Issue 1, pp. 141 - 151
Journal Article
Magnetic Resonance in Medicine, ISSN 0740-3194, 05/2017, Volume 77, Issue 5, pp. 1818 - 1830
Quantitative T.sub.1 mapping is a magnetic resonance imaging technique that estimates the spin-lattice relaxation time of tissues. Even though T.sub.1 mapping... 
super‐resolution | reconstruction | mapping | relaxometry | super-resolution | Image resolution | High resolution | Magnetic resonance imaging | Noise | Spatial discrimination | Mapping | Resonance | Spatial resolution | Relaxation time | Image acquisition | Index Medicus
Journal Article
Journal of the American Society of Echocardiography, ISSN 0894-7317, 2016, Volume 29, Issue 1, pp. A23 - A24
Journal Article
2005, ISBN 9780387258904, xii, 239
Book
2012, ISBN 1849950466, xviii, 230
Book
IEEE Transactions on Pattern Analysis and Machine Intelligence, ISSN 0162-8828, 02/2016, Volume 38, Issue 2, pp. 295 - 307
We propose a deep learning method for single image super-resolution (SR). Our method directly learns an end-to-end mapping between the low/high-resolution... 
Convolutional codes | Training | Image resolution | Super-resolution | Neural networks | Feature extraction | deep convolutional neural networks | sparse coding | Image reconstruction | QUALITY ASSESSMENT | COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE | ENGINEERING, ELECTRICAL & ELECTRONIC | Usage | Analysis
Journal Article
2013 IEEE International Conference on Computer Vision, ISSN 1550-5499, 12/2013, pp. 945 - 952
Super resolution (SR) algorithms typically assume that the blur kernel is known (either the Point Spread Function 'PSF' of the camera, or some default low-pass... 
Training | Image resolution | Databases | Estimation | Cameras | Approximation methods | Kernel | Kernels | Computer vision | Algorithms | Conferences | Low pass filters | Deterioration | Images | Blinds
Conference Proceeding
2001, The Kluwer international series in engineering and computer science, ISBN 9780792374718, Volume SECS 632., xiii, 279
Book