World-known computer manufacturers are making efforts to develop high-grade typesetting systems. In 1993, the Zhuhai Stone Computer Typesetting System Development Company and Beijing Stone Modem Typesetting and Printi...World-known computer manufacturers are making efforts to develop high-grade typesetting systems. In 1993, the Zhuhai Stone Computer Typesetting System Development Company and Beijing Stone Modem Typesetting and Printing Equipment Company, both subsidiaries of the Beijing Stone Group, marketed the Stone Quick and Easy Tvpesetting Module and展开更多
Stone typesetting is an applied technology, which is a very important link in the construction process. According to the construction drawings and the site and the size of the stone board, the most reasonable size of ...Stone typesetting is an applied technology, which is a very important link in the construction process. According to the construction drawings and the site and the size of the stone board, the most reasonable size of the finished stone is divided and typeset and numbered according to the direction of the stone texture and the transition of color. In addition to effectively avoiding stone waste and saving material cost, the color typesetting of stone materials;More importantly, it can ensure the final landing effect of the stone and avoid the occurrence of non-striation.展开更多
In the PSP(Pressure-Sensitive Paint),image deblurring is essential due to factors such as prolonged camera exposure times and highmodel velocities,which can lead to significant image blurring.Conventional deblurring m...In the PSP(Pressure-Sensitive Paint),image deblurring is essential due to factors such as prolonged camera exposure times and highmodel 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 or attention-based techniques.In this paper,we introduce a novel deblurring architecture featuring multiple DAAM(Deformable Ack Attention Module).These modules provide enhanced flexibility for end-to-end deblurring,leveraging irregular convolution operations for efficient feature extraction while employing attention mechanisms interpreted as multiple 1×1 convolutions,subsequently reassembled to enhance performance.Furthermore,we incorporate a RSC(Residual Shortcut Convolution)module for initial feature processing,aimed at reducing redundant computations and improving the learning capacity for representative shallow features.To preserve critical spatial information during upsampling and downsampling,we replace conventional convolutions with wt(Haar wavelet downsampling)and dysample(Upsampling by Dynamic Sampling).This modification significantly enhances high-precision image reconstruction.By integrating these advanced modules within an encoder-decoder framework,we present the DFDNet(Deformable Fusion Deblurring Network)for image blur removal,providing robust technical support for subsequent PSP data analysis.Experimental evaluations on the FY dataset demonstrate the superior performance of our model,achieving competitive results on the GOPRO and HIDE datasets.展开更多
文摘World-known computer manufacturers are making efforts to develop high-grade typesetting systems. In 1993, the Zhuhai Stone Computer Typesetting System Development Company and Beijing Stone Modem Typesetting and Printing Equipment Company, both subsidiaries of the Beijing Stone Group, marketed the Stone Quick and Easy Tvpesetting Module and
文摘Stone typesetting is an applied technology, which is a very important link in the construction process. According to the construction drawings and the site and the size of the stone board, the most reasonable size of the finished stone is divided and typeset and numbered according to the direction of the stone texture and the transition of color. In addition to effectively avoiding stone waste and saving material cost, the color typesetting of stone materials;More importantly, it can ensure the final landing effect of the stone and avoid the occurrence of non-striation.
基金supported by the National Natural Science Foundation of China(No.12202476).
文摘In the PSP(Pressure-Sensitive Paint),image deblurring is essential due to factors such as prolonged camera exposure times and highmodel 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 or attention-based techniques.In this paper,we introduce a novel deblurring architecture featuring multiple DAAM(Deformable Ack Attention Module).These modules provide enhanced flexibility for end-to-end deblurring,leveraging irregular convolution operations for efficient feature extraction while employing attention mechanisms interpreted as multiple 1×1 convolutions,subsequently reassembled to enhance performance.Furthermore,we incorporate a RSC(Residual Shortcut Convolution)module for initial feature processing,aimed at reducing redundant computations and improving the learning capacity for representative shallow features.To preserve critical spatial information during upsampling and downsampling,we replace conventional convolutions with wt(Haar wavelet downsampling)and dysample(Upsampling by Dynamic Sampling).This modification significantly enhances high-precision image reconstruction.By integrating these advanced modules within an encoder-decoder framework,we present the DFDNet(Deformable Fusion Deblurring Network)for image blur removal,providing robust technical support for subsequent PSP data analysis.Experimental evaluations on the FY dataset demonstrate the superior performance of our model,achieving competitive results on the GOPRO and HIDE datasets.
基金国家高技术研究发展计划(863)(the National High-Tech Research and Development Plan of China under Grant No.2003AA1Z2110 No.2004AA1Z2500)新疆维吾尔自治区高技术研究与发展计划项目(No.200412108)。
