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Keywords = computerized tomography denoising

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16 pages, 1724 KB  
Article
WaveletDFDS-Net: A Dual Forward Denoising Stream Network for Low-Dose CT Noise Reduction
by Yusheng Zhou, Zhengmin Kong, Tao Huang, Euijoon Ahn, Hao Li and Li Ding
Electronics 2024, 13(10), 1906; https://doi.org/10.3390/electronics13101906 - 13 May 2024
Cited by 1 | Viewed by 2391
Abstract
The challenge of denoising low-dose computed tomography (CT) has garnered significant research interest due to the detrimental impact of noise on CT image quality, impeding diagnostic accuracy and image-guided therapies. This paper introduces an innovative approach termed the Wavelet Domain Dual Forward Denoising [...] Read more.
The challenge of denoising low-dose computed tomography (CT) has garnered significant research interest due to the detrimental impact of noise on CT image quality, impeding diagnostic accuracy and image-guided therapies. This paper introduces an innovative approach termed the Wavelet Domain Dual Forward Denoising Stream Network (WaveletDFDS-Net) to address this challenge. This method ingeniously combines convolutional neural networks and Transformers to leverage their complementary capabilities in feature extraction. Additionally, it employs a wavelet transform for efficient image downsampling, thereby preserving critical information while reducing computational requirements. Moreover, we have formulated a distinctive dual-domain compound loss function that significantly enhances the restoration of intricate details. The performance of WaveletDFDS-Net is assessed by comparative experiments conducted on public CT datasets, and results demonstrate its enhanced denoising effect with an SSIM of 0.9269, PSNR of 38.1343 and RMSE of 0.0130, superior to existing methods. Full article
(This article belongs to the Special Issue Pattern Recognition and Machine Learning Applications, 2nd Edition)
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24 pages, 69336 KB  
Article
A Non-Convex Fractional-Order Differential Equation for Medical Image Restoration
by Chenwei Li and Donghong Zhao
Symmetry 2024, 16(3), 258; https://doi.org/10.3390/sym16030258 - 20 Feb 2024
Cited by 7 | Viewed by 2008
Abstract
We propose a new non-convex fractional-order Weber multiplicative denoising variational generalized function, which leads to a new fractional-order differential equation, and prove the existence of a unique solution to this equation. Furthermore, the model is solved using the partial differential equation (PDE) method [...] Read more.
We propose a new non-convex fractional-order Weber multiplicative denoising variational generalized function, which leads to a new fractional-order differential equation, and prove the existence of a unique solution to this equation. Furthermore, the model is solved using the partial differential equation (PDE) method and the alternating direction multiplier method (ADMM) to verify the theoretical results. The proposed model is tested on some symmetric and asymmetric medical computerized tomography (CT) images, and the experimental results show that the combination of the fractional-order differential equation and the Weber function has better performance in medical image restoration than the traditional model. Full article
(This article belongs to the Special Issue Image Processing and Symmetry: Topics and Applications)
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