Compressive Sensing for the Photonic Mixer Device [electronic resource] : Fundamentals, Methods and Results /
Heredia Conde, Miguel.author.
Miguel Heredia Conde aims at finding novel ways to fit the valuable mathematical results of the Compressive Sensing (CS) theory to the specific case of the Photonic Mixer Device (PMD).To this end, methods are presented that take profit of the sparsity of the signals gathered by PMD sensors. In his research, the author reveals that CS enables outstanding tradeoffs between sensing effort and depth error reduction or resolution enhancement. Contents Phase-Shift-Based Time-of-Flight Imaging Systems Fundamentals of Compressive Sensing High Resolution Characterization of PMD Pixels Compressive Sensing in Spatial and Time-Frequency Domains CS-PMD: A Compressive Sensing ToF Camera based on the Photonic Mixer Device Target GroupsIncludes bibliographical references.
Phase-Shift-Based Time-of-Flight Imaging Systems -- Fundamentals of Compressive Sensing -- High Resolution Characterization of PMD Pixels -- Compressive Sensing in Spatial and Time-Frequency Domains -- CS-PMD: A Compressive Sensing ToF Camera based on the Photonic Mixer Device.
Miguel Heredia Conde aims at finding novel ways to fit the valuable mathematical results of the Compressive Sensing (CS) theory to the specific case of the Photonic Mixer Device (PMD).To this end, methods are presented that take profit of the sparsity of the signals gathered by PMD sensors. In his research, the author reveals that CS enables outstanding tradeoffs between sensing effort and depth error reduction or resolution enhancement. Contents Phase-Shift-Based Time-of-Flight Imaging Systems Fundamentals of Compressive Sensing High Resolution Characterization of PMD Pixels Compressive Sensing in Spatial and Time-Frequency Domains CS-PMD: A Compressive Sensing ToF Camera based on the Photonic Mixer Device Target GroupsOptical data processing.
Data structures (Computer science).
Applied mathematics.
Engineering mathematics.
Computer Imaging, Vision, Pattern Recognition and Graphics.
Data Structures and Information Theory.
Mathematical and Computational Engineering.