Kelly, Kevin F.2014-08-082014-08-082008Takhar, Dharmpal. "Compressed Sensing for Imaging Applications." (2008) Diss., Rice University. <a href="https://hdl.handle.net/1911/76508">https://hdl.handle.net/1911/76508</a>.https://hdl.handle.net/1911/76508Compressed sensing is a new sampling theory which allows reconstructing signals using sub-Nyquist measurements. This can significantly reduce the computation required for both image and video whether during acquisition or encoding, especially at the sensor. Compressed sensing works on the assumption of sparsity of the signal in some known domain, which is incoherent with the measurement domain. We exploit this technique to build a single pixel camera using an optical modulator and a single photosensor. Random projections of the signal (image) are applied to the optical modulator, which has a random matrix displayed on it corresponding to the measurement domain (random noise). This random projected signal is focused and summed at the photosensor and will be later used for reconstructing the signal. In this scheme, a tradeoff between the spatial extent of sampling array and a sequential sampling over time with a single detector is performed. In addition to the single sensor method, we will also demonstrate a new design which allows compressive im90 ppapplication/pdfengCopyright is held by the author, unless otherwise indicated. Permission to reuse, publish, or reproduce the work beyond the bounds of fair use or other exemptions to copyright law must be obtained from the copyright holder.Electrical EngineeringComputer engineeringCompressed Sensing for Imaging ApplicationsThesisTakharD