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

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 performance is highly affected by noise level data. A singular value decomposition (SVD) based plug and play priors method was proposed this work robustness shown be superior as compared total variation regularization, basis pursuit deconvolution Lanczos Tikhonov regularization provided improved case numerical experimental cases show that improvement can high 8.1 dB signal ratio reconstructed image 67.98% root mean square error comparison state art methods.