Results 41 to 50 of about 412,052 (160)

Hypergraph-Supervised Deep Subspace Clustering

Mathematics, 2021
Auto-encoder (AE)-based deep subspace clustering (DSC) methods aim to partition high-dimensional data into underlying clusters, where each cluster corresponds to a subspace. As a standard module in current AE-based DSC, the self-reconstruction cost plays
Yu Hu, Hongmin Cai
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Deep learning methods for high-resolution microscale light field image reconstruction: a survey

Frontiers in Bioengineering and Biotechnology
Deep learning is progressively emerging as a vital tool for image reconstruction in light field microscopy. The present review provides a comprehensive examination of the latest advancements in light field image reconstruction techniques based on deep ...
Bingzhi Lin, Yuan Tian, Yue Zhang, Zhijing Zhu, Depeng Wang   +4 more
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Deep learning-based Intraoperative MRI reconstruction

European Radiology Experimental
Abstract Background We retrospectively evaluated the quality of deep learning (DL) reconstructions of on-scanner accelerated intraoperative MRI (iMRI) during respective brain tumor surgery. Methods Accelerated iMRI was performed using dual surface coils positioned around ...
Jon André Ottesen, Tryggve Storas, Svein Are Sirirud Vatnehol, Grethe Løvland, Einar Osland Vik-Mo, Till Schellhorn, Karoline Skogen, Christopher Larsson, Atle Bjørnerud, Inge Rasmus Groote-Eindbaas, Matthan W. A. Caan   +10 more
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Spectral imaging with deep learning

Light: Science & Applications, 2022
This review categorizes deep-learning-based computational spectral imaging methods and provides insight into amplitude, phase, and wavelength-based light encoding strategies for deep-learning spectral reconstruction.
Longqian Huang, Ruichen Luo, Xu Liu, Xiang Hao   +3 more
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Image Reconstruction Using Deep Learning

, 2018
This paper proposes a deep learning architecture that attains statistically significant improvements over traditional algorithms in Poisson image denoising espically when the noise is strong. Poisson noise commonly occurs in low-light and photon- limited settings, where the noise can be most accurately modeled by the Poission distribution.
Liu, Po-Yu, Lam, Edmund Y.
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Overview of image-based 3D reconstruction technology

Journal of the European Optical Society-Rapid Publications
Three-dimensional (3D) reconstruction technology is the key technology to establish and express the objective world by using computer, and it is widely used in real 3D, automatic driving, aerospace, navigation and industrial robot applications. According
Niu Yuandong, Liu Limin, Huang Fuyu, Huang Siyuan, Chen Shuangyou   +4 more
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A Deep Recursive Cascaded Convolutional Network for Parallel MRI

Chinese Journal of Magnetic Resonance, 2019
Fast magnetic resonance imaging (MRI) has been attracting more and more research interests in recent years. With the emergence of big data and development of advanced deep learning algorithms, neural network has become a common and effective tool for ...
CHENG Hui-tao, WANG Shan-shan, KE Zi-wen, JIA Sen, CHENG Jing, QIU Zhi-lang, ZHENG Hai-rong, LIANG Dong   +7 more
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Deep learning reconstruction for brain diffusion-weighted imaging: efficacy for image quality improvement, apparent diffusion coefficient assessment, and intravoxel incoherent motion evaluation in in vitro and in vivo studies

Diagnostic and Interventional Radiology, 2023
PURPOSEDeep learning reconstruction (DLR) to improve imaging quality has already been introduced, but no studies have evaluated the effect of DLR on diffusion-weighted imaging (DWI) or intravoxel incoherent motion (IVIM) in in vitro or in vivo studies ...
Satomu Hanamatsu, Kazuhiro Murayama, Yoshiharu Ohno, Kaori Yamamoto, Masao Yui, Hiroshi Toyama   +5 more
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LAVA HyperSense and deep-learning reconstruction for near-isotropic (3D) enhanced magnetic resonance enterography in patients with Crohn’s disease: utility in noise reduction and image quality improvement

Diagnostic and Interventional Radiology, 2023
PURPOSEThis study aimed to compare near-isotropic contrast-enhanced T1-weighted (CE-T1W) magnetic resonance enterography (MRE) images reconstructed with vendor-supplied deep-learning reconstruction (DLR) with those reconstructed conventionally in terms ...
Jung Hee Son, Yedaun Lee, Ho-Joon Lee, Joonsung Lee, Hyunwoong Kim, Marc R. Lebel   +5 more
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Deep-learning for 3D reconstruction

, 2021
Depth perception is paramount for many computer vision applications such as autonomous driving and augmented reality. Despite active sensors (e.g., LiDAR, Time-of-Flight, struc- tured light) are quite diffused, they have severe shortcomings that could be potentially addressed by image-based sensors. Concerning this latter category, deep learning has
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