Abstract
The photoacoustic tomography (PAT) method, based on compressive sensing (CS)
theory, requires that, for the CS reconstruction, the desired image should have a
sparse representation in a known transform domain. However, the sparsity of
photoacoustic signals is destroyed because noises always exist. Therefore, the
original sparse signal cannot be effectively recovered using the general
reconstruction algorithm. In this study, Bayesian compressive sensing (BCS) is
employed to obtain highly sparse representations of photoacoustic images based on a
set of noisy CS measurements. Results of simulation demonstrate that the
BCS-reconstructed image can achieve superior performance than other state-of-the-art
CS-reconstruction algorithms.
© 2011 Chinese Optics Letters
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