Yuehao Wu, (University of Delaware), “Compressive optical imaging systems”, Advisor: Prof. Dennis W. Prather, 2012. Compared to the classic Nyquist sampling theorem, Compressed Sensing or Compressive Sampling (CS) was proposed as a more efficient alternative for sampling sparse signals. In this dissertation, the author discusses the implementation of the CS theory in building a variety of optical imaging systems. CS-based Imaging Systems (CSISs) exploit the sparsity of optical images in their transformed domains by imposing incoherent CS measurement patterns on them. The amplitudes and locations of sparse frequency components of optical images in their transformed domains can be reconstructed from the CS measurement results by solving an l₁ -regularized minimization problem. This thesis presents two hardware implementation schemes for CSISs, including a single pixel detector based scheme and an array detector based scheme. The first implementation scheme is suitable for acquiring Two-Dimensional (2D) spatial information of the imaging scene. We demonstrate the feasibility of this implementation scheme by developing a single pixel camera, a multispectral imaging system, and an optical sectioning microscope for fluorescence microscopy. The array detector based scheme is suitable for hyperspectral imaging applications, wherein both the spatial and spectral information of the imaging scene are of interest. We demonstrate the feasibility of this scheme by developing a Digital Micromirror Device-based Snapshot Spectral Imaging (DMD-SSI) system, which implements CS measurement processes on the Three-Dimensional (3D) spatial/spectral information of the imaging scene. For details, please visit here or contact the author.