2019/03/05 by Drazen Jelic, Jelic, Drazen, Ana Scekic +5
Computer Science · Engineering · #Distributed Sensor Networks and Detection Algorithms #FOS: Electrical engineering #Microwave Imaging and Scattering Analysis #Photoacoustic and Ultrasonic Imaging #Signal Processing (eess.SP) #Sparse and Compressive Sensing Techniques #electronic engineering #information engineering
paper · pdf · doi:10.48550/arxiv.1903.01826
openalex publication_date 2019/03/05 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28
The Compressive Sensing (CS) as a novel acquisition approach that finds its\nusage in image processing. The hypothesis like this one assures signal recovery\nwith high quality from decreased number of samples compared with the number\nrequired by the Nyquist - Shannon sampling theorem. It includes a gathering of\nstrategies for representing a signal that are based on the predetermined number\nof estimations and after that signal reconstruction. The CS has been broadly\nutilized and applied in numerous applications including computed tomography,\nWiFi communication, image processing and camera design. Complex mathematics is\ndeveloped in order to ensure signal reconstruction from relatively small\ninformation. Two commonly used groups of the algorithms are convex optimization\nand greedy approaches.\n