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\n \n 2022\n \n \n (1)\n \n \n
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\n\n \n \n \n \n \n Low-dose CT image denoising using deep convolutional neural networks with extended receptive fields.\n \n \n \n\n\n \n Trung, N.; Trinh, D.; Trung, N.; and Luong, M\n\n\n \n\n\n\n
Signal, Image and Video Processing, 16(7): 1963–1971. 2022.\n
Query date: 2025-11-20 21:08:29\n\n
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\n\n \n\n \n\n \n link\n \n \n\n bibtex\n \n\n \n\n \n\n \n \n \n \n \n \n \n\n \n \n \n\n\n\n
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@article{pop00001,\n\tauthor = {NT Trung and DH Trinh and NL Trung and M Luong},\n\ttype = {Journal article},\n\ttitle = {Low-dose CT image denoising using deep convolutional neural networks with extended receptive fields},\n\tjournal = {Signal, Image and Video Processing},\n\tcitation = {https://scholar.google.com/citations?view_op=view_citation\\&hl=en\\&user=xiQm9VQAAAAJ\\&pagesize=100\\&citation_for_view=xiQm9VQAAAAJ:2osOgNQ5qMEC},\n\tyear = {2022},\n\tvolume = {16},\n\tnumber = {7},\n\tpages = {1963--1971},\n\tnote = {36 cites: https://scholar.google.com/scholar?oi=bibs\\&hl=en\\&cites=18193807448387744539},\n\tnote = {Query date: 2025-11-20 21:08:29}\n}\n\n
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\n \n 2020\n \n \n (2)\n \n \n
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\n\n \n \n \n \n \n Dilated Residual Convolutional Neural Networks for Low-Dose CT Image Denoising.\n \n \n \n\n\n \n Thanh Trung, N.; Trinh, D.; Linh Trung, N.; Thi Thuy Quynh, T.; and Luu, M.\n\n\n \n\n\n\n In
2020 IEEE Asia Pacific Conference on Circuits and Systems (APCCAS), pages 189-192, 2020. \n
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\n\n \n\n \n \n doi\n \n \n\n \n link\n \n \n\n bibtex\n \n\n \n\n \n\n \n \n \n \n \n \n \n\n \n \n \n \n \n\n\n\n
\n
@INPROCEEDINGS{9301693,\n author={Thanh Trung, Nguyen and Trinh, Dinh-Hoan and Linh Trung, Nguyen and Thi Thuy Quynh, Tran and Luu, Manh-Ha},\n booktitle={2020 IEEE Asia Pacific Conference on Circuits and Systems (APCCAS)}, \n title={Dilated Residual Convolutional Neural Networks for Low-Dose CT Image Denoising}, \n year={2020},\n volume={},\n number={},\n pages={189-192},\n keywords={Computed tomography;Noise reduction;Training;Image denoising;X-ray imaging;Residual neural networks;Image reconstruction;Computer tomography;low dose imaging;medical image denoising;dilated residual network;convolutional neural network;perceptual loss},\n doi={10.1109/APCCAS50809.2020.9301693}}\n\n\n
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\n\n \n \n \n \n \n Low-dose CT image denoising using image decomposition and sparse representation.\n \n \n \n\n\n \n Trung, N.; Hoan, T.; Trung, N.; and Luong, M\n\n\n \n\n\n\n
REV Journal on Electronics and Communications 9 (3-4). 2020.\n
Query date: 2025-11-20 21:08:29\n\n
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\n\n \n\n \n\n \n link\n \n \n\n bibtex\n \n\n \n\n \n\n \n \n \n \n \n \n \n\n \n \n \n\n\n\n
\n
@article{pop00003,\n\tauthor = {NT Trung and TD Hoan and NL Trung and M Luong},\n\ttype = {Journal article},\n\ttitle = {Low-dose CT image denoising using image decomposition and sparse representation},\n\tjournal = {REV Journal on Electronics and Communications 9 (3-4)},\n\tcitation = {https://scholar.google.com/citations?view_op=view_citation\\&hl=en\\&user=xiQm9VQAAAAJ\\&pagesize=100\\&citation_for_view=xiQm9VQAAAAJ:u-x6o8ySG0sC},\n\tyear = {2020},\n\tnote = {7 cites: https://scholar.google.com/scholar?oi=bibs\\&hl=en\\&cites=13474400358610216912},\n\tnote = {Query date: 2025-11-20 21:08:29}\n}\n\n
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\n \n 2019\n \n \n (1)\n \n \n
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\n \n \n
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\n\n \n \n \n \n \n Robust Denoising of Low-Dose CT Images using Convolutional Neural Networks.\n \n \n \n\n\n \n Trung, N. T.; Dinh Hoan, T.; Trung, N. L.; and Manh Ha, L.\n\n\n \n\n\n\n In
2019 6th NAFOSTED Conference on Information and Computer Science (NICS), pages 506-511, 2019. \n
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\n\n \n\n \n \n doi\n \n \n\n \n link\n \n \n\n bibtex\n \n\n \n\n \n\n \n \n \n \n \n \n \n\n \n \n \n \n \n\n\n\n
\n
@INPROCEEDINGS{9023861,\n author={Trung, Nguyen Thanh and Dinh Hoan, Trinh and Trung, Nguyen Linh and Manh Ha, Luu},\n booktitle={2019 6th NAFOSTED Conference on Information and Computer Science (NICS)}, \n title={Robust Denoising of Low-Dose CT Images using Convolutional Neural Networks}, \n year={2019},\n volume={},\n number={},\n pages={506-511},\n keywords={Computed tomography;Noise reduction;Image reconstruction;X-ray imaging;Testing;Image denoising;Training;low-dose CT;convolutional neural network;perception loss},\n doi={10.1109/NICS48868.2019.9023861}}\n\n\n
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\n \n 2016\n \n \n (1)\n \n \n
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\n\n \n \n \n \n \n An efficient example-based method for CT image denoising based on frequency decomposition and sparse representation.\n \n \n \n\n\n \n Nguyen, T.; Trinh, D.; and Linh-Trung, N.\n\n\n \n\n\n\n In
2016 International Conference on Advanced Technologies for Communications (ATC), pages 293–296, 2016. IEEE\n
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\n\n \n\n \n\n \n link\n \n \n\n bibtex\n \n\n \n\n \n\n \n \n \n \n \n \n \n\n \n \n \n\n\n\n
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@inproceedings{nguyen2016efficient,\n title={An efficient example-based method for CT image denoising based on frequency decomposition and sparse representation},\n author={Nguyen, Thanh-Trung and Trinh, Dinh-Hoan and Linh-Trung, Nguyen},\n booktitle={2016 International Conference on Advanced Technologies for Communications (ATC)},\n pages={293--296},\n year={2016},\n organization={IEEE}\n}\n\n\n
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\n \n 2014\n \n \n (1)\n \n \n
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\n\n \n \n \n \n \n An effective example-based denoising method for CT images using Markov random field.\n \n \n \n\n\n \n Trinh, D.; Nguyen, T.; and Linh-Trung, N.\n\n\n \n\n\n\n In
2014 International Conference on Advanced Technologies for Communications (ATC 2014), pages 355–359, 2014. IEEE\n
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@inproceedings{trinh2014effective,\n title={An effective example-based denoising method for CT images using Markov random field},\n author={Trinh, Dinh-Hoan and Nguyen, Thanh-Trung and Linh-Trung, Nguyen},\n booktitle={2014 International Conference on Advanced Technologies for Communications (ATC 2014)},\n pages={355--359},\n year={2014},\n organization={IEEE}\n}\n\n
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