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10.1016/j.cmpb.2021.106193 http://scihub22266oqcxt.onion/10.1016/j.cmpb.2021.106193 34107373!8142806!34107373     free     free     free Deprecated: Implicit conversion from float 219.6 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534       Comput+Methods+Programs+Biomed 2021 ; 208 (ä): 106193 Nephropedia Template TP {{tp|p=34107373|t=2021. Progressive back-projection network for COVID-CT super-resolution |pdf=|usr=}}{{34107373}} gab.com Text Progressive back-projection network for COVID-CT super-resolution JO: Comput Methods Programs Biomed http://www.kidney.de/pget.php?&tw=1&pmid=34107373 #FOAvip BACKGROUND AND OBJECTIVE: Recently, the COVID-19 epidemic has become more and more serious around the world, how to improve the image resolution of COVID-CT is a very important task. The network based on progressive upsampling for COVID-CT super-resolution increases the reconstruction error. This paper proposes a progressive back-projection network (PBPN) for COVID-CT super-resolution to solve this problem. METHODS: In this paper, we propose a progressive back-projection network (PBPN) for COVID-CT super-resolution. PBPN is divided into two stages, and each stage consists of back-projection, deep feature extraction and upscaling. We design an up-projection and down-projection residual module to minimize the reconstruction error and construct a residual attention module to extract deep features. In each stage, firstly, PBPN performs back-projection to extract shallow features by two up-projection and down-projection residual modules; then, PBPN extracts deep features from the shallow features by two residual attention modules; finally, PBPN upsamples the deep features through sub-pixel convolution. RESULTS: The proposed method achieves the improvements of about 0.14~0.47 dB/0.0012~0.0060 for x 2 scale factor, 0.02~0.08 dB/0.0024~0.0059 for x 3 scale factor, and 0.08~0.41 dB/ 0.0040~0.0147 for x 4 scale factor than state-of-the-art methods (Bicubic, SRCNN, FSRCNN, VDSR, LapSRN, DRCN and DSRN) in terms of PSNR/SSIM on benchmark datasets. CONCLUSIONS: The proposed mehtod obtains better performance for COVID-CT super-resolution and reconstructs high-quality high-resolution COVID-CT images that contain more details and edges. Twit Text FOAVip Progressive back-projection network for COVID-CT super-resolution ????Comput Methods Programs Biomed http://www.kidney.de/pget.php?&tw=1&pmid=34107373 #FOAvip Twit Text # Free Paper on # # # # # LINK http://www.kidney.de/pget.php?&tw=1&pmid=34107373 #FOAvip English Wikipedia <ref>{{Cite pmid|34107373}}</ref> Progressive back-projection network for COVID-CT super-resolution #MMPMID34107373 Song Z; Zhao X; Hui Y; Jiang H Comput Methods Programs Biomed 2021[Sep]; 208 (ä): 106193 PMID34107373show ga BACKGROUND AND OBJECTIVE: Recently, the COVID-19 epidemic has become more and more serious around the world, how to improve the image resolution of COVID-CT is a very important task. The network based on progressive upsampling for COVID-CT super-resolution increases the reconstruction error. This paper proposes a progressive back-projection network (PBPN) for COVID-CT super-resolution to solve this problem. METHODS: In this paper, we propose a progressive back-projection network (PBPN) for COVID-CT super-resolution. PBPN is divided into two stages, and each stage consists of back-projection, deep feature extraction and upscaling. We design an up-projection and down-projection residual module to minimize the reconstruction error and construct a residual attention module to extract deep features. In each stage, firstly, PBPN performs back-projection to extract shallow features by two up-projection and down-projection residual modules; then, PBPN extracts deep features from the shallow features by two residual attention modules; finally, PBPN upsamples the deep features through sub-pixel convolution. RESULTS: The proposed method achieves the improvements of about 0.14~0.47 dB/0.0012~0.0060 for x 2 scale factor, 0.02~0.08 dB/0.0024~0.0059 for x 3 scale factor, and 0.08~0.41 dB/ 0.0040~0.0147 for x 4 scale factor than state-of-the-art methods (Bicubic, SRCNN, FSRCNN, VDSR, LapSRN, DRCN and DSRN) in terms of PSNR/SSIM on benchmark datasets. CONCLUSIONS: The proposed mehtod obtains better performance for COVID-CT super-resolution and reconstructs high-quality high-resolution COVID-CT images that contain more details and edges. |*COVID-19[MESH] |*Image Processing, Computer-Assisted[MESH] |Algorithms[MESH] |Humans[MESH] |Neural Networks, Computer[MESH] |SARS-CoV-2[MESH]Progressive back-projection network for COVID-CT super-resolution (epmc).pdf DeepDyve Pubget Overpricing