Date of Award
Doctor of Philosophy
Many compressive sensing architectures have shown promise towards reducingthe bandwidth for image acquisition significantly. In order to use these architectures for video acquisition we need a scheme that is able to effectively exploit temporal redundancies in a sequence. In this thesis we study a method to efficiently sample and reconstruct specific video sequences. The method is suitable for implementation using a single pixel detector along with a digital micromirror device (DMD) or other forms of spatial light modulators (SLMs). Conventional implementations of single pixel cameras are able to spatially compress the signal but the compressed measurements make it difficult to exploit temporal redundancies directly. Moreover a single pixel camera needs to make measurements in a sequential manner before the scene changes making it inefficient for video imaging. In this thesis we discuss a measurement scheme that exploits sparsity along the time axis for video imaging. After acquiring all measurements required for the first frame, measurements are only acquired from the areas which change in subsequent frames. We segment the first frame and detect magnitude and direction of change for each segment and acquire compressed measurements for the changing segments in the predicted direction. TV minimization is used to reconstruct the dynamic areas and PSNR variation is studied against different parameters of proposed scheme. We show the reconstruction results for a few test sequences commonly used for performance analysis and demonstrate the practical utility of the scheme. A comparison is made with existing algorithms to show the eeffectiveness of proposed method for specific video sequences. We also discuss use of customized transform to improve reconstruction of submililimeter wave single pixel imager. We use a sparseness inducing transformation onthe measurements and optimize the result using l1 minimization algorithms. We demonstrate improvement in result of several images acquired and reconstructed using this technique.
dissertation or thesis originally submitted to the local University of Memphis Electronic Theses & dissertation (ETD) Repository.
Noor, Imama, "An Adaptive Optimal Bandwidth Sensor for Video Imaging and Sparsifying Basis" (2013). Electronic Theses and Dissertations. 653.