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Abstract #2620

Utilizing the Wavelet Transform's Structure in Compressed Sensing

Nicholas Dwork1, Daniel O'Connor2, Corey A. Baron3, Ethan M. I. Johnson4, John M. Pauly5, and Peder E.Z. Larson6
1Radiology and Biomedical Imaging, UCSF, San Francisco, CA, United States, 2Mathematics and Statistics, University of San Francisco, San Francisco, CA, United States, 3Robarts Research, Western University, London, ON, Canada, 4Biomedical Engineering, Northwestern University, Evanston, IL, United States, 5Electrical Engineering, Stanford University, Stanford, CA, United States, 6Radiology and Biomedical Imaging, University of California in San Francisco, San Francisco, CA, United States

In this work, we present a modification of the standard implementation of compressed sensing that takes advantage of the structure of the Daubechies wavelet transform. By doing so, we show that we retain additional detail in the reconstructed images when few data samples are used.

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