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Discrepancy-Based Analysis Of Measurement Sampling Points In Compressive Sensing, Felipe Batista Da Silva
Discrepancy-Based Analysis Of Measurement Sampling Points In Compressive Sensing, Felipe Batista Da Silva
Open Access Theses & Dissertations
Compressive sensing (CS) is a technique in signal processing that under certain conditions allows someone to reconstruct sparse signals from fewer linear measurements. A problem in CS is modeled in terms of an underdetermined linear system, whose associated matrix is previously designed. Then, it is of interest in CS to know what a good sampling defined by the sensing matrix is and how to measure it. In this work, we provided analytical proofs of properties of the metric discrepancy that allow us to propose a fast algorithm for discrepancy calculation. Such metric measures the quality of the sampling measurement points …
Application Of Compressive Sensing To Microwave Tomography, Rafael Gerardo Lopez
Application Of Compressive Sensing To Microwave Tomography, Rafael Gerardo Lopez
Open Access Theses & Dissertations
Microwave tomography(MT) allows the safe, non-intrusive examination of the internal structureof an object by irradiating it with microwave signals and measuring how they get attenuated and diffracted as they propagate through the object. Measurement of these signals is difficult and slow, it depends on the objectâ??s physical properties and the test setup used for this. MT is based on a scanning process that yields measurement data with unique characteristics that make it difficult to process using standard computational methods. As the number of measurements needed for an image with acceptable resolution increases, the complexity of processing all the data becomes …
Compressive Vector Reconstruction: Hypothesis For Blind Image Deconvolution, Alonso Orea Amador
Compressive Vector Reconstruction: Hypothesis For Blind Image Deconvolution, Alonso Orea Amador
Open Access Theses & Dissertations
Alternative imaging devices propose to acquire and compress images simultaneously. These devices are based on the compressive sensing (CS) theory. A reduction in the measurement required for reconstruction without a post-compression sub-system allows imaging devices to become simpler, smaller, and cheaper. In this research, we propose a new algorithm to compress and reconstruct blurred images for CS imaging devices. Blur effect in images is common due to relative motion, lens, limited aperture dimensions, lack of focus, and/or atmospheric turbulence. Our intention is to compress a blurred image with CS techniques and then reconstruct a blur-free version using the proposed algorithm. …