Zobrazeno 1 - 10
of 134
pro vyhledávání: '"Hu, YuYang"'
Autor:
Hu, Yuyang, Peng, Albert, Gan, Weijie, Milanfar, Peyman, Delbracio, Mauricio, Kamilov, Ulugbek S.
Deep neural networks trained as image denoisers are widely used as priors for solving imaging inverse problems. While Gaussian denoising is thought sufficient for learning image priors, we show that priors from deep models pre-trained as more general
Externí odkaz:
http://arxiv.org/abs/2410.02057
Autor:
Hu, Yuyang, Kothapalli, Satya V. V. N., Gan, Weijie, Sukstanskii, Alexander L., Wu, Gregory F., Goyal, Manu, Yablonskiy, Dmitriy A., Kamilov, Ulugbek S.
We introduce a new framework called DiffGEPCI for cross-modality generation in magnetic resonance imaging (MRI) using a 2.5D conditional diffusion model. DiffGEPCI can synthesize high-quality Fluid Attenuated Inversion Recovery (FLAIR) and Magnetizat
Externí odkaz:
http://arxiv.org/abs/2311.18073
There is a growing interest in model-based deep learning (MBDL) for solving imaging inverse problems. MBDL networks can be seen as iterative algorithms that estimate the desired image using a physical measurement model and a learned image prior speci
Externí odkaz:
http://arxiv.org/abs/2311.02003
Image denoisers have been shown to be powerful priors for solving inverse problems in imaging. In this work, we introduce a generalization of these methods that allows any image restoration network to be used as an implicit prior. The proposed method
Externí odkaz:
http://arxiv.org/abs/2310.01391
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
Autor:
Gan, Weijie, Ying, Chunwei, Eshraghi, Parna, Wang, Tongyao, Eldeniz, Cihat, Hu, Yuyang, Liu, Jiaming, Chen, Yasheng, An, Hongyu, Kamilov, Ulugbek S.
Deep equilibrium models (DEQ) have emerged as a powerful alternative to deep unfolding (DU) for image reconstruction. DEQ models-implicit neural networks with effectively infinite number of layers-were shown to achieve state-of-the-art image reconstr
Externí odkaz:
http://arxiv.org/abs/2210.03837
Autor:
Hu, Yuyang, Gan, Weijie, Ying, Chunwei, Wang, Tongyao, Eldeniz, Cihat, Liu, Jiaming, Chen, Yasheng, An, Hongyu, Kamilov, Ulugbek S.
Deep model-based architectures (DMBAs) integrating physical measurement models and learned image regularizers are widely used in parallel magnetic resonance imaging (PMRI). Traditional DMBAs for PMRI rely on pre-estimated coil sensitivity maps (CSMs)
Externí odkaz:
http://arxiv.org/abs/2210.02584
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
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
Publikováno v:
In Talanta 1 June 2024 273
