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.

中文翻译：

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尺寸减少，即插即用，可改善噪声数据有限的光声层析成像

本质上，通过迭代方法来解决具有有限噪声数据的光声层析成像的不适定逆问题，以提供准确的解决方案。这些方法的性能受到光声数据中噪声水平的很大影响。在这项工作中，提出了一种基于奇异值分解（SVD）的即插即用先验方法来解决光声逆问题，从而为数据中的噪声提供鲁棒性。与总变异正则化，基追踪反卷积和基于Lanczos Tikhonov的正则化相比，该方法被证明是优越的，并且在有噪声数据的情况下提供了改进的性能。数值和实验案例表明，改进后的重建图像信噪比可提高至8.1 dB，最高可达67 dB。