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<p>Photoacoustic imaging signals, characterized as non-stationary signals with pulsed features, are often compromised by severe noise interference from detector noise, environmental factors, and optical scattering within deep tissues, leading to reduced image quality. To address this challenge, we propose a novel denoising method for photoacoustic signals based on gradient-and-sparsity-regularized Stockwell transform(ST), inspired by the extended spectral characteristics in the ST domain. This method effectively suppresses noise while preserving essential signal features. Results from signal processing and photoacoustic image reconstruction demonstrate that the proposed approach outperforms algorithms such as compressed sensing with total variation(CS-TV) denoising, wavelet transform(WT) denoising,and Gaussian(GS) denoising in terms of mean squared error(MSE), structural similarity index(SSIM), and peak signal-to-noise ratio(PSNR). Notably, it exhibits exceptional denoising performance under high-noise conditions.</p>

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An S-transform regularization-based denoising method for acoustic signals in photoacoustics

  • 期刊论文

  • 发表日期：

    2026年1月8日

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  • Haoyu Wang,
  • Yiping Han

摘要

Photoacoustic imaging signals, characterized as non-stationary signals with pulsed features, are often compromised by severe noise interference from detector noise, environmental factors, and optical scattering within deep tissues, leading to reduced image quality. To address this challenge, we propose a novel denoising method for photoacoustic signals based on gradient-and-sparsity-regularized Stockwell transform(ST), inspired by the extended spectral characteristics in the ST domain. This method effectively suppresses noise while preserving essential signal features. Results from signal processing and photoacoustic image reconstruction demonstrate that the proposed approach outperforms algorithms such as compressed sensing with total variation(CS-TV) denoising, wavelet transform(WT) denoising,and Gaussian(GS) denoising in terms of mean squared error(MSE), structural similarity index(SSIM), and peak signal-to-noise ratio(PSNR). Notably, it exhibits exceptional denoising performance under high-noise conditions.