Results 31 to 40 of about 412,052 (160)
Deep Learning for PET Image Reconstruction [PDF]
IEEE Transactions on Radiation and Plasma Medical Sciences, 2021 This article reviews the use of a subdiscipline of artificial intelligence (AI), deep learning , for the reconstruction of images in positron emission tomography (PET). Deep learning can be used either directly or as a component of conventional reconstruction, in order to reconstruct images from noisy PET data.
Andrew J. Reader +5 more
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IEEE Access, 2019 The traditional image to enlarge algorithms include nearest neighbor interpolation, bilinear interpolation and high-order interpolation. In order to achieve super-resolution reconstruction of images, a new algorithm combining traditional algorithms and ...
Na Sun, Huina Li
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Thermal Image Reconstruction Using Deep Learning [PDF]
IEEE Access, 2020 A high-resolution thermal camera is very expensive and is thus difficult to be used. Furthermore, thermal images become blurred in various cases of object motion, camera shaking, and camera defocusing. To solve these problems, a previous super-resolution restoration (SRR) technique converting a thermal image acquired by a low-resolution camera into a ...
Ganbayar Batchuluun +4 more
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WDLReconNet: Compressive Sensing Reconstruction With Deep Learning Over Wireless Fading Channels
IEEE Access, 2019 Deep learning has been exploited in compressive sensing to reduce the computational complexity of reconstruction algorithms. However, existing deep-learning-based reconstruction algorithms might result in poor performance, when applied in wireless ...
Hancheng Lu, Lei Bo
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POCS-Augmented CycleGAN for MR Image Reconstruction
Applied Sciences, 2021 Recent years have seen increased research interest in replacing the computationally intensive Magnetic resonance (MR) image reconstruction process with deep neural networks.
Yiran Li +5 more
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Path Asymmetry Reconstruction via Deep Learning
2022 IEEE 21st Mediterranean Electrotechnical Conference (MELECON), 2022 This paper proposes a novel scheme to enhance the accuracy of packet-switched network synchronization systems by estimating path asymmetry (PA) using convolutional denoising autoencoders (CDAEs). Network synchronization is a key enabler of several emerging applications, with increasingly tight accuracy requirements especially for 5G.
Alhashmi, N, Almoosa, N, Gianini, G
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Review of Super-Resolution Image Reconstruction Algorithms [PDF]
Jisuanji kexue yu tansuo, 2022 In human visual perception system, high-resolution (HR) image is an important medium to clearly express its spatial structure, detailed features, edge texture and other information, and it has a very wide range of practical value in medicine, criminal ...
ZHONG Mengyuan, JIANG Lin
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Domain Transform Network for Photoacoustic Tomography from Limited-view and Sparsely Sampled Data
Photoacoustics, 2020 Medical image reconstruction methods based on deep learning have recently demonstrated powerful performance in photoacoustic tomography (PAT) from limited-view and sparse data.
Tong Tong +7 more
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Complexities of deep learning-based undersampled MR image reconstruction
European Radiology Experimental, 2023 Artificial intelligence has opened a new path of innovation in magnetic resonance (MR) image reconstruction of undersampled k-space acquisitions. This review offers readers an analysis of the current deep learning-based MR image reconstruction methods ...
Constant Richard Noordman +4 more
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IEEE Access, 2018 In this paper, the objective is to classify biomedical signals from their compressive measurements. The problem arises when compressed sensing (CS) is used for energy efficient acquisition and transmission of such signals for wireless body area network ...
Vanika Singhal +2 more
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