Ruizhe Yu¹, Tingrui Yue³, Lei Liang^2,3,*, Zhisheng Gao^1,*

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 >

Jiaxiang Wang¹, Zhengyi Li¹, Peng Shi¹, Hongying Yu², Dongbai Sun^1,3,*

CMC-Computers, Materials & Continua, Vol.79, No.1, pp. 1187-1204, 2024, DOI:10.32604/cmc.2024.046929 - 25 April 2024

Abstract Scanning electron microscopy (SEM) is a crucial tool in the field of materials science, providing valuable insights into the microstructural characteristics of materials. Unfortunately, SEM images often suffer from blurriness caused by improper hardware calibration or imaging automation errors, which present challenges in analyzing and interpreting material characteristics. Consequently, rectifying the blurring of these images assumes paramount significance to enable subsequent analysis. To address this issue, we introduce a Material Images Deblurring Network (MIDNet) built upon the foundation of the Nonlinear Activation Free Network (NAFNet). MIDNet is meticulously tailored to address the blurring in images… More >

Jinxing Niu^{1, *}, Yajie Jiang¹, Yayun Fu¹, Tao Zhang¹, Nicola Masini²

CMC-Computers, Materials & Continua, Vol.65, No.1, pp. 807-816, 2020, DOI:10.32604/cmc.2020.011044 - 23 July 2020

Abstract Video surveillance system is used in various fields such as transportation and social life. The bad weather can lead to the degradation of the video surveillance image quality. In rainy environment, the raindrops and the background are mixed, which lead to make the image degradation, so the removal of the raindrops has great significance for image restoration. In this article, after analyzing the inter-frame difference method in detecting and removing raindrops, a background difference method is proposed based on Gaussian model. In this method, the raindrop is regarded as a moving object relative to the… More >