Output details
15 - General Engineering
University of Edinburgh (joint submission with Heriot-Watt University)
Iterative hard thresholding for compressed sensing
This paper describes a novel family of efficient and near optimal compressed sensing (CS) algorithms, offering scalable reconstruction for sub-Nyquist sensing applications without the need for convex optimization. The algorithm has been adopted and modified by leading CS research groups: e.g. Stanford (DoI:10.1109/JSTSP.2009.2039176 ); Technion, (DoI:10.1109/TSP.2011.2174985); and Rice University (DoI:10.1109/TIT.2012.2234823). It has been highly influential and been extended to more general signal models and measurements, e.g. model-based CS of Prof Baraniuk (richb@rice.edu) Rice university (DoI:10.1109/TIT.2010.2040894), and rank minimization (DoI:10.1007/s10208-011-9084-6). It also substantially contributed to the award of the EPSRC grant (EP/F039697/1) on the topic.
