Turnitin
降AI改写
早检测系统
早降重系统
Turnitin-UK版
万方检测-期刊版
维普编辑部版
Grammarly检测
Paperpass检测
checkpass检测
PaperYY检测
Deep learning for tomographic image reconstruction
Deep-learning-based tomographic imaging is an important application of artificial
intelligence and a new frontier of machine learning. Deep learning has been widely used in …
intelligence and a new frontier of machine learning. Deep learning has been widely used in …
AI-based reconstruction for fast MRI—A systematic review and meta-analysis
Compressed sensing (CS) has been playing a key role in accelerating the magnetic
resonance imaging (MRI) acquisition process. With the resurgence of artificial intelligence …
resonance imaging (MRI) acquisition process. With the resurgence of artificial intelligence …
Adaptive diffusion priors for accelerated MRI reconstruction
Deep MRI reconstruction is commonly performed with conditional models that de-alias
undersampled acquisitions to recover images consistent with fully-sampled data. Since …
undersampled acquisitions to recover images consistent with fully-sampled data. Since …
Plug-and-play methods for integrating physical and learned models in computational imaging: Theory, algorithms, and applications
Plug-and-play (PnP) priors constitute one of the most widely used frameworks for solving
computational imaging problems through the integration of physical models and learned …
computational imaging problems through the integration of physical models and learned …
Unsupervised MRI reconstruction via zero-shot learned adversarial transformers
Supervised reconstruction models are characteristically trained on matched pairs of
undersampled and fully-sampled data to capture an MRI prior, along with supervision …
undersampled and fully-sampled data to capture an MRI prior, along with supervision …
Deep equilibrium architectures for inverse problems in imaging
Recent efforts on solving inverse problems in imaging via deep neural networks use
architectures inspired by a fixed number of iterations of an optimization method. The number …
architectures inspired by a fixed number of iterations of an optimization method. The number …
Self‐supervised learning of physics‐guided reconstruction neural networks without fully sampled reference data
Purpose To develop a strategy for training a physics‐guided MRI reconstruction neural
network without a database of fully sampled data sets. Methods Self‐supervised learning via …
network without a database of fully sampled data sets. Methods Self‐supervised learning via …
Flot: Scene flow on point clouds guided by optimal transport
We propose and study a method called FLOT that estimates scene flow on point clouds. We
start the design of FLOT by noticing that scene flow estimation on point clouds reduces to …
start the design of FLOT by noticing that scene flow estimation on point clouds reduces to …
Self-supervised neural networks for spectral snapshot compressive imaging
We consider using untrained neural networks to solve the reconstruction problem of
snapshot compressive imaging (SCI), which uses a two-dimensional (2D) detector to …
snapshot compressive imaging (SCI), which uses a two-dimensional (2D) detector to …
Gradient step denoiser for convergent plug-and-play
Plug-and-Play methods constitute a class of iterative algorithms for imaging problems where
regularization is performed by an off-the-shelf denoiser. Although Plug-and-Play methods …
regularization is performed by an off-the-shelf denoiser. Although Plug-and-Play methods …