Zobrazeno 1 - 10
of 607
pro vyhledávání: '"Xu, Xiaojian"'
A trained attention-based transformer network can robustly recover the complex topologies given by the Richtmyer-Meshkoff instability from a sequence of hydrodynamic features derived from radiographic images corrupted with blur, scatter, and noise. T
Externí odkaz:
http://arxiv.org/abs/2408.00985
Shorter SPECT Scans Using Self-supervised Coordinate Learning to Synthesize Skipped Projection Views
Purpose: This study addresses the challenge of extended SPECT imaging duration under low-count conditions, as encountered in Lu-177 SPECT imaging, by developing a self-supervised learning approach to synthesize skipped SPECT projection views, thus sh
Externí odkaz:
http://arxiv.org/abs/2406.18840
Diffusion models can learn strong image priors from underlying data distribution and use them to solve inverse problems, but the training process is computationally expensive and requires lots of data. Such bottlenecks prevent most existing works fro
Externí odkaz:
http://arxiv.org/abs/2406.02462
Model-based methods play a key role in the reconstruction of compressed sensing (CS) MRI. Finding an effective prior to describe the statistical distribution of the image family of interest is crucial for model-based methods. Plug-and-play (PnP) is a
Externí odkaz:
http://arxiv.org/abs/2405.03854
Regularization by Denoising (RED) is a well-known method for solving image restoration problems by using learned image denoisers as priors. Since the regularization parameter in the traditional RED does not have any physical interpretation, it does n
Externí odkaz:
http://arxiv.org/abs/2311.03819
Phase retrieval (PR) is a crucial problem in many imaging applications. This study focuses on resolving the holographic phase retrieval problem in situations where the measurements are affected by a combination of Poisson and Gaussian noise, which co
Externí odkaz:
http://arxiv.org/abs/2305.07712
Autor:
Xu, Xiaojian, Gan, Weijie, Kothapalli, Satya V. V. N., Yablonskiy, Dmitriy A., Kamilov, Ulugbek S.
Quantitative MRI (qMRI) refers to a class of MRI methods for quantifying the spatial distribution of biological tissue parameters. Traditional qMRI methods usually deal separately with artifacts arising from accelerated data acquisition, involuntary
Externí odkaz:
http://arxiv.org/abs/2210.06330
Plug-and-Play Priors (PnP) and Regularization by Denoising (RED) are widely-used frameworks for solving imaging inverse problems by computing fixed-points of operators combining physical measurement models and learned image priors. While traditional
Externí odkaz:
http://arxiv.org/abs/2205.13051
Regularization by denoising (RED) is a widely-used framework for solving inverse problems by leveraging image denoisers as image priors. Recent work has reported the state-of-the-art performance of RED in a number of imaging applications using pre-tr
Externí odkaz:
http://arxiv.org/abs/2202.04961
The past few years have seen a surge of activity around integration of deep learning networks and optimization algorithms for solving inverse problems. Recent work on plug-and-play priors (PnP), regularization by denoising (RED), and deep unfolding h
Externí odkaz:
http://arxiv.org/abs/2202.02388