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10.1016/j.cmpb.2021.105934 http://scihub22266oqcxt.onion/10.1016/j.cmpb.2021.105934 33454574!7834190!33454574     free     free     free Deprecated: Implicit conversion from float 217.6 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534 Deprecated: Implicit conversion from float 217.6 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534 Deprecated: Implicit conversion from float 217.6 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534 Deprecated: Implicit conversion from float 217.6 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534 Deprecated: Implicit conversion from float 217.6 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534       Comput+Methods+Programs+Biomed 2021 ; 200 (ä): 105934 Nephropedia Template TP {{tp|p=33454574|t=2021. Multi-window back-projection residual networks for reconstructing COVID-19 CT super-resolution images |pdf=|usr=}}{{33454574}} gab.com Text Multi-window back-projection residual networks for reconstructing COVID-19 CT super-resolution images JO: Comput Methods Programs Biomed http://www.kidney.de/pget.php?&tw=1&pmid=33454574 #FOAvip BACKGROUND AND OBJECTIVE: With the increasing problem of coronavirus disease 2019 (COVID-19) in the world, improving the image resolution of COVID-19 computed tomography (CT) becomes a very important task. At present, single-image super-resolution (SISR) models based on convolutional neural networks (CNN) generally have problems such as the loss of high-frequency information and the large size of the model due to the deep network structure. METHODS: In this work, we propose an optimization model based on multi-window back-projection residual network (MWSR), which outperforms most of the state-of-the-art methods. Firstly, we use multi-window to refine the same feature map at the same time to obtain richer high/low frequency information, and fuse and filter out the features needed by the deep network. Then, we develop a back-projection network based on the dilated convolution, using up-projection and down-projection modules to extract image features. Finally, we merge several repeated and continuous residual modules with global features, merge the information flow through the network, and input them to the reconstruction module. RESULTS: The proposed method shows the superiority over the state-of-the-art methods on the benchmark dataset, and generates clear COVID-19 CT super-resolution images. CONCLUSION: Both subjective visual effects and objective evaluation indicators are improved, and the model specifications are optimized. Therefore, the MWSR method can improve the clarity of CT images of COVID-19 and effectively assist the diagnosis and quantitative assessment of COVID-19. Twit Text FOAVip Multi-window back-projection residual networks for reconstructing COVID-19 CT super-resolution images ????Comput Methods Programs Biomed http://www.kidney.de/pget.php?&tw=1&pmid=33454574 #FOAvip Twit Text # Free Paper on # # # # # LINK http://www.kidney.de/pget.php?&tw=1&pmid=33454574 #FOAvip English Wikipedia <ref>{{Cite pmid|33454574}}</ref> Multi-window back-projection residual networks for reconstructing COVID-19 CT super-resolution images #MMPMID33454574 Qiu D; Cheng Y; Wang X; Zhang X Comput Methods Programs Biomed 2021[Mar]; 200 (ä): 105934 PMID33454574show ga BACKGROUND AND OBJECTIVE: With the increasing problem of coronavirus disease 2019 (COVID-19) in the world, improving the image resolution of COVID-19 computed tomography (CT) becomes a very important task. At present, single-image super-resolution (SISR) models based on convolutional neural networks (CNN) generally have problems such as the loss of high-frequency information and the large size of the model due to the deep network structure. METHODS: In this work, we propose an optimization model based on multi-window back-projection residual network (MWSR), which outperforms most of the state-of-the-art methods. Firstly, we use multi-window to refine the same feature map at the same time to obtain richer high/low frequency information, and fuse and filter out the features needed by the deep network. Then, we develop a back-projection network based on the dilated convolution, using up-projection and down-projection modules to extract image features. Finally, we merge several repeated and continuous residual modules with global features, merge the information flow through the network, and input them to the reconstruction module. RESULTS: The proposed method shows the superiority over the state-of-the-art methods on the benchmark dataset, and generates clear COVID-19 CT super-resolution images. CONCLUSION: Both subjective visual effects and objective evaluation indicators are improved, and the model specifications are optimized. Therefore, the MWSR method can improve the clarity of CT images of COVID-19 and effectively assist the diagnosis and quantitative assessment of COVID-19. |Algorithms[MESH] |COVID-19/*diagnostic imaging[MESH] |Deep Learning[MESH] |Humans[MESH] |Image Enhancement/*methods[MESH] |SARS-CoV-2[MESH]Multi-window back-projection residual networks for reconstructing COVI(epmc).pdf DeepDyve Pubget Overpricing