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  • Open Access

    ARTICLE

    Multi-Scale Dilated Attention-Based Transformer Network for Image Inpainting

    Jinrong Li1,2, Chunhua Wei2, Lei Liang2,3,*, Zhisheng Gao1,*

    CMC-Computers, Materials & Continua, Vol.84, No.2, pp. 3259-3280, 2025, DOI:10.32604/cmc.2025.063547 - 03 July 2025

    Abstract The Pressure Sensitive Paint Technique (PSP) has gained attention in recent years because of its significant benefits in measuring surface pressure on wind tunnel models. However, in the post-processing process of PSP images, issues such as pressure taps, paint peeling, and contamination can lead to the loss of pressure data on the image, which seriously affects the subsequent calculation and analysis of pressure distribution. Therefore, image inpainting is particularly important in the post-processing process of PSP images. Deep learning offers new methods for PSP image inpainting, but some basic characteristics of convolutional neural networks (CNNs)… More >

  • Open Access

    ARTICLE

    Blur-Deblur Algorithm for Pressure-Sensitive Paint Image Based on Variable Attention Convolution

    Ruizhe Yu1, Tingrui Yue3, Lei Liang2,3,*, Zhisheng Gao1,*

    CMC-Computers, Materials & Continua, Vol.82, No.3, pp. 5239-5256, 2025, DOI:10.32604/cmc.2025.059077 - 06 March 2025

    Abstract In the PSP (Pressure-Sensitive Paint), image deblurring is essential due to factors such as prolonged camera exposure times and high model velocities, which can lead to significant image blurring. Conventional deblurring methods applied to PSP images often suffer from limited accuracy and require extensive computational resources. To address these issues, this study proposes a deep learning-based approach tailored for PSP image deblurring. Considering that PSP applications primarily involve the accurate pressure measurements of complex geometries, the images captured under such conditions exhibit distinctive non-uniform motion blur, presenting challenges for standard deep learning models utilizing convolutional… More >

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