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Dimensionality reduced plug and play priors for improving photoacoustic tomographic imaging with limited noisy data

Awasthi, N and Kalva, SK and Pramanik, M and Yalavarthy, PK (2021) Dimensionality reduced plug and play priors for improving photoacoustic tomographic imaging with limited noisy data. In: Biomedical Optics Express, 12 (3). pp. 1320-1338.

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Official URL: https://dx.doi.org/10.1364/BOE.415182


The reconstruction methods for solving the ill-posed inverse problem of photoacoustic tomography with limited noisy data are iterative in nature to provide accurate solutions. These methods performance is highly affected by the noise level in the photoacoustic data. A singular value decomposition (SVD) based plug and play priors method for solving photoacoustic inverse problem was proposed in this work to provide robustness to noise in the data. The method was shown to be superior as compared to total variation regularization, basis pursuit deconvolution and Lanczos Tikhonov based regularization and provided improved performance in case of noisy data. The numerical and experimental cases show that the improvement can be as high as 8.1 dB in signal to noise ratio of the reconstructed image and 67.98 in root mean square error in comparison to the state of the art methods. © 2021 Optical Society of America under the terms of the OSA Open Access Publishing Agreement.

Item Type: Journal Article
Publication: Biomedical Optics Express
Publisher: OSA - The Optical Society
Additional Information: The copyright of this article belongs to OSA - The Optical Society
Keywords: Image enhancement; Image reconstruction; Iterative methods; Mean square error; Numerical methods; Photoacoustic effect; Signal to noise ratio; Singular value decomposition; Tomography, ILL-posed inverse problem; Photoacoustic tomography; Reconstructed image; Reconstruction method; Root mean square errors; State-of-the-art methods; Tomographic imaging; Total variation regularization, Inverse problems
Department/Centre: Division of Interdisciplinary Sciences > Computational and Data Sciences
Date Deposited: 12 Mar 2021 16:07
Last Modified: 12 Mar 2021 16:07
URI: http://eprints.iisc.ac.in/id/eprint/68183

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