The recently emerging technique of sparse reconstruction has received much attention in the field of photoacoustic imaging (PAI). Compressed sensing (CS) has large potential in efficiently reconstructing high-quality PAI images with sparse sampling signal. In this article, we propose a CS-based error-tolerant regularized smooth L0 (ReSL0) algorithm for PAI image reconstruction, which has the same computational advantages as the SL0 algorithm while having a higher degree of immunity to inaccuracy caused by noise. In order to evaluate the performance of the ReSL0 algorithm, we reconstruct the simulated dataset obtained from three phantoms. In addition, a real experimental dataset from agar phantom is also used to verify the effectiveness of the ReSL0 algorithm. Compared to three L₀ norm, L₁ norm, and TV norm-based CS algorithms for signal recovery and image reconstruction, experiments demonstrated that the ReSL0 algorithm provides a good balance between the quality and efficiency of reconstructions. Furthermore, the PSNR of the reconstructed image calculated by the introduced method was better than the other three methods. In particular, it can notably improve reconstruction quality in the case of noisy measurement.

中文翻译：

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一种基于正则平滑L0范数最小化的光声成像算法

最近新兴的稀疏重建技术在光声成像（PAI）领域受到了广泛关注。压缩感知（CS）在利用稀疏采样信号有效重建高质量 PAI 图像方面具有巨大潜力。在本文中，我们提出了一种用于 PAI 图像重建的基于 CS 的容错正则化平滑 L0 (ReSL0) 算法，该算法具有与 SL0 算法相同的计算优势，同时对噪声引起的不准确具有更高的免疫力。为了评估 ReSL0 算法的性能，我们重建了从三个模型获得的模拟数据集。此外，还使用来自琼脂体模的真实实验数据集来验证ReSL0算法的有效性。与用于信号恢复和图像重建的三种基于L ₀范数、L ₁范数和TV范数的CS算法相比，实验表明ReSL0算法在重建质量和效率之间提供了良好的平衡。此外，该方法计算出的重建图像的PSNR优于其他三种方法。特别是在有噪声测量的情况下，它可以显着提高重建质量。