Image reconstruction by domain-transform manifold learning

Letter Image reconstruction by domain-transform manifold learning Bo Zhu1,2,3, Jeremiah Z. Liu4, Stephen F. Cauley1,2, Bruce R. Rosen1,2 & Matthew S. Rosen1,2,3. Image reconstruction is essential for imaging applications across Inspired by the perceptual learning archetype, we describe here the physical and life sciences, including optical and radar systems, a data-driven unified Image reconstruction approach, which we magnetic resonance imaging, X-ray computed tomography, call AUTOMAP, that learns a reconstruction mapping between the positron emission tomography, ultrasound imaging and radio sensor-domain data and Image -domain output (Fig.)

parameter tuning to optimize reconstruction performance. Here we present a unified framework for image reconstruction— automated transform by manifold approximation (AUTOMAP)— which recasts image reconstruction as a data-driven supervised learning task that allows a mapping between the sensor and the

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