Scholarlink

<p>With the widespread use of industrial computed tomography (CT) in semiconductor manufacturing, the noise in tomographic images from low-power radiation sources has become an increasingly prominent issue, which severely impacts the detection and processing tasks. In this paper, a spatial-frequency image noise suppression model (SFNS) is proposed to suppress noise while maintaining structural details in the image. A hybrid loss with spatial-frequency dual-domain information is designed to resolve spatial-domain limitations in optimization through complex-discrepancy-based spherical region separation criterion. To address the coupled noise characteristics in low-power CT systems, a stochastic noise degradation training strategy is designed to dynamically emulate fluctuations in real noise environments, and the network architecture integrates checkerboard sampling with cascaded residual stacks to enhance detail perception and reduce computational overhead. Experimental results demonstrate that SFNS achieves PSNR 32.14dB and SSIM 0.8896. Both quantitative metrics and qualitative evaluations validate the effectiveness of the proposed method in balancing structural fidelity and noise suppression.</p>

错误：搜索内容不能为空，请输入英文关键词
错误：关键词超出字数限制，请精简
高级检索

SFNS: Spatial-frequency image noise suppression for low-power industrial cone-beam computed tomography

  • 期刊论文

  • 发表日期：

    2025年6月20日

查看全文
  • Yimin Zhu,
  • Xing Wu,
  • Junfeng Yao,
  • Quan Qian,
  • Jun Song,
  • Shouwei Gao

摘要

With the widespread use of industrial computed tomography (CT) in semiconductor manufacturing, the noise in tomographic images from low-power radiation sources has become an increasingly prominent issue, which severely impacts the detection and processing tasks. In this paper, a spatial-frequency image noise suppression model (SFNS) is proposed to suppress noise while maintaining structural details in the image. A hybrid loss with spatial-frequency dual-domain information is designed to resolve spatial-domain limitations in optimization through complex-discrepancy-based spherical region separation criterion. To address the coupled noise characteristics in low-power CT systems, a stochastic noise degradation training strategy is designed to dynamically emulate fluctuations in real noise environments, and the network architecture integrates checkerboard sampling with cascaded residual stacks to enhance detail perception and reduce computational overhead. Experimental results demonstrate that SFNS achieves PSNR 32.14dB and SSIM 0.8896. Both quantitative metrics and qualitative evaluations validate the effectiveness of the proposed method in balancing structural fidelity and noise suppression.