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Optimizing computed tomography image reconstruction for focal hepatic lesions: Deep learning image reconstruction vs iterative reconstruction. [PDF]
HeliyonBackground: Deep learning image reconstruction (DLIR) is a novel computed tomography (CT) reconstruction technique that minimizes image noise, enhances image quality, and enables radiation dose reduction.
Jaruvongvanich V +10 more
europepmc +2 more sources
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
doaj +1 more source
Review of Image Super-resolution Reconstruction Algorithms Based on Deep Learning [PDF]
Jisuanji kexue yu tansuo, 2022 The essence of image super-resolution reconstruction technology is to break through the limitation of hardware conditions, and reconstruct a high-resolution image from a low-resolution image which contains less infor-mation through the image super ...
YANG Caidong, LI Chengyang, LI Zhongbo, XIE Yongqiang, SUN Fangwei, QI Jin
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Journal of Reconstructive Microsurgery Open, 2023 Objective We report the case of a 40-year-old man with an inveterate ulnar nerve neuroma following a laceration injury of his left wrist twenty-three years ago.
Laura A. Hruby +6 more
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Nuclear TV Multi-channel Image Reconstruction Algorithm Based on Chambolle-pock Framework
CT Lilun yu yingyong yanjiu, 2022 Total variation (TV) minimum algorithm is an image reconstruction algorithm based on compressed sensing theory, which can realize the reconstruction of images with high accuracy from sparse projection or noisy projection data and has been widely used in ...
Jingyi MA, Zhiwei QIAO
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Journal of Electrical and Computer Engineering, 2022 Due to the large noise and many discrete points of the image in the traditional image reconstruction process, the reconstruction quality of the image deviates greatly from the actual target.
Jing He
doaj +1 more source
Direct PET Image Reconstruction Incorporating Deep Image Prior and a Forward Projection Model [PDF]
IEEE Trans. Radiat. Plasma Med. Sci. 6 (2022) 841, 2021 Convolutional neural networks (CNNs) have recently achieved remarkable performance in positron emission tomography (PET) image reconstruction. In particular, CNN-based direct PET image reconstruction, which directly generates the reconstructed image from the sinogram, has potential applicability to PET image enhancements because it does not require ...
arxiv +1 more source
Value of Virtual Reality Technology in Image Inspection and 3D Geometric Modeling
IEEE Access, 2020 Aiming at the poor expressive ability of image statistical information during the reconstruction process of traditional 3D image reconstruction method based on virtual reality technology, resulting in low accuracy of 3D image after reconstruction, a new ...
Longyu Lu, Jinkai Ma, Shuying Qu
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Deep learning-based video stream reconstruction in mass-production diffractive optical systems [PDF]
Компьютерная оптика, 2021 Many recent studies have focused on developing image reconstruction algorithms in optical systems based on flat optics. These studies demonstrate the feasibility of applying a combination of flat optics and the reconstruction algorithms in real vision ...
V. Evdokimova +12 more
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Variational semi-blind sparse deconvolution with orthogonal kernel bases and its application to MRFM [PDF]
, 2013 We present a variational Bayesian method of joint image reconstruction and point spread function (PSF) estimation when the PSF of the imaging device is only partially known.
Almeida +39 more
core +10 more sources