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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