Zobrazeno 1 - 10
of 15
pro vyhledávání: '"Shoushtari, Shirin"'
Autor:
Zou, Zihao, Shoushtari, Shirin, Liu, Jiaming, Zhang, Jialiang, Judge, Patrick, Santana, Emilia, Lim, Alison, Foston, Marcus, Kamilov, Ulugbek S.
Nuclear Magnetic Resonance (NMR) spectroscopy is a widely-used technique in the fields of bio-medicine, chemistry, and biology for the analysis of chemicals and proteins. The signals from NMR spectroscopy often have low signal-to-noise ratio (SNR) du
Externí odkaz:
http://arxiv.org/abs/2405.11064
Autor:
Shoushtari, Shirin, Chandler, Edward P., Zhang, Jialiang, Senanayake, Manjula, Pingali, Sai Venkatesh, Foston, Marcus, Kamilov, Ulugbek S.
Small Angle Neutron Scattering (SANS) is a non-destructive technique utilized to probe the nano- to mesoscale structure of materials by analyzing the scattering pattern of neutrons. Accelerating SANS acquisition for in-situ analysis is essential, but
Externí odkaz:
http://arxiv.org/abs/2403.10495
Publikováno v:
2023 IEEE 9th International Workshop on Computational Advances in Multi-Sensor Adaptive Processing (CAMSAP), 2023, pg. 186-190
Plug-and-Play Priors (PnP) is a well-known class of methods for solving inverse problems in computational imaging. PnP methods combine physical forward models with learned prior models specified as image denoisers. A common issue with the learned mod
Externí odkaz:
http://arxiv.org/abs/2403.10374
Plug-and-Play (PnP) priors is a widely-used family of methods for solving imaging inverse problems by integrating physical measurement models with image priors specified using image denoisers. PnP methods have been shown to achieve state-of-the-art p
Externí odkaz:
http://arxiv.org/abs/2310.00133
Plug-and-play (PnP) prior is a well-known class of methods for solving imaging inverse problems by computing fixed-points of operators combining physical measurement models and learned image denoisers. While PnP methods have been extensively used for
Externí odkaz:
http://arxiv.org/abs/2305.12672
Phase retrieval refers to the problem of recovering an image from the magnitudes of its complex-valued linear measurements. Since the problem is ill-posed, the recovery requires prior knowledge on the unknown image. We present DOLPH as a new deep mod
Externí odkaz:
http://arxiv.org/abs/2211.00529
Deep model-based architectures (DMBAs) are widely used in imaging inverse problems to integrate physical measurement models and learned image priors. Plug-and-play priors (PnP) and deep equilibrium models (DEQ) are two DMBA frameworks that have recei
Externí odkaz:
http://arxiv.org/abs/2211.00531
There is a growing interest in deep model-based architectures (DMBAs) for solving imaging inverse problems by combining physical measurement models and learned image priors specified using convolutional neural nets (CNNs). For example, well-known fra
Externí odkaz:
http://arxiv.org/abs/2207.13200
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
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