Industrial Computed Tomography Image Denoising Network Based on Channel Attention Mechanism
In industrial computed tomography (CT), using noisy projection data for reconstruction increases the noise in the reconstructed image and reduces the signal-to-noise ratio (SNR). When the quality of projection data is poor, classical denoising and reconstruction algorithms are ineffective in removin...
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| Format: | Article |
| Language: | English |
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Editorial Office of Computerized Tomography Theory and Application
2025-07-01
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| Series: | CT Lilun yu yingyong yanjiu |
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| Online Access: | https://www.cttacn.org.cn/cn/article/doi/10.15953/j.ctta.2025.068 |
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| _version_ | 1849428264131493888 |
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| author | Yu HE Chengxiang WANG Wei YU |
| author_facet | Yu HE Chengxiang WANG Wei YU |
| author_sort | Yu HE |
| collection | DOAJ |
| description | In industrial computed tomography (CT), using noisy projection data for reconstruction increases the noise in the reconstructed image and reduces the signal-to-noise ratio (SNR). When the quality of projection data is poor, classical denoising and reconstruction algorithms are ineffective in removing the noise. To improve the quality of low signal-to-noise CT reconstructed images, this study proposes a deep learning-based denoising method. The method integrates squeeze-and-excitation blocks into the decoder phase and adaptively adjusts the weights of the channels to better preserve structural details during the denoising process. Experimental results demonstrate that the proposed method significantly reduces the noise and effectively preserves edge details, outperforming other comparative methods in both visual quality and quantitative values. |
| format | Article |
| id | doaj-art-e0b4899c9fa84eeb879d1cb6fbb4e842 |
| institution | Kabale University |
| issn | 1004-4140 |
| language | English |
| publishDate | 2025-07-01 |
| publisher | Editorial Office of Computerized Tomography Theory and Application |
| record_format | Article |
| series | CT Lilun yu yingyong yanjiu |
| spelling | doaj-art-e0b4899c9fa84eeb879d1cb6fbb4e8422025-08-20T03:28:44ZengEditorial Office of Computerized Tomography Theory and ApplicationCT Lilun yu yingyong yanjiu1004-41402025-07-0134454355010.15953/j.ctta.2025.0682025-068Industrial Computed Tomography Image Denoising Network Based on Channel Attention MechanismYu HE0Chengxiang WANG1Wei YU2School of Mathematical Sciences, Chongqing Normal University, Chongqing 401331, ChinaSchool of Mathematical Sciences, Chongqing Normal University, Chongqing 401331, ChinaKey Laboratory of Optoelectronic Sensing and Intelligent Control, Hubei University of Science and Technology, Xianning 437100, ChinaIn industrial computed tomography (CT), using noisy projection data for reconstruction increases the noise in the reconstructed image and reduces the signal-to-noise ratio (SNR). When the quality of projection data is poor, classical denoising and reconstruction algorithms are ineffective in removing the noise. To improve the quality of low signal-to-noise CT reconstructed images, this study proposes a deep learning-based denoising method. The method integrates squeeze-and-excitation blocks into the decoder phase and adaptively adjusts the weights of the channels to better preserve structural details during the denoising process. Experimental results demonstrate that the proposed method significantly reduces the noise and effectively preserves edge details, outperforming other comparative methods in both visual quality and quantitative values.https://www.cttacn.org.cn/cn/article/doi/10.15953/j.ctta.2025.068deep learningchannel attention mechanismindustrial ctlow snrimage denoising |
| spellingShingle | Yu HE Chengxiang WANG Wei YU Industrial Computed Tomography Image Denoising Network Based on Channel Attention Mechanism CT Lilun yu yingyong yanjiu deep learning channel attention mechanism industrial ct low snr image denoising |
| title | Industrial Computed Tomography Image Denoising Network Based on Channel Attention Mechanism |
| title_full | Industrial Computed Tomography Image Denoising Network Based on Channel Attention Mechanism |
| title_fullStr | Industrial Computed Tomography Image Denoising Network Based on Channel Attention Mechanism |
| title_full_unstemmed | Industrial Computed Tomography Image Denoising Network Based on Channel Attention Mechanism |
| title_short | Industrial Computed Tomography Image Denoising Network Based on Channel Attention Mechanism |
| title_sort | industrial computed tomography image denoising network based on channel attention mechanism |
| topic | deep learning channel attention mechanism industrial ct low snr image denoising |
| url | https://www.cttacn.org.cn/cn/article/doi/10.15953/j.ctta.2025.068 |
| work_keys_str_mv | AT yuhe industrialcomputedtomographyimagedenoisingnetworkbasedonchannelattentionmechanism AT chengxiangwang industrialcomputedtomographyimagedenoisingnetworkbasedonchannelattentionmechanism AT weiyu industrialcomputedtomographyimagedenoisingnetworkbasedonchannelattentionmechanism |