Zobrazeno 1 - 10
of 254
pro vyhledávání: '"KAMILOV, Ulugbek"'
Autor:
Park, Chicago Y., Hu, Yuyang, McCann, Michael T., Garcia-Cardona, Cristina, Wohlberg, Brendt, Kamilov, Ulugbek S.
Plug-and-play (PnP) methods are extensively used for solving imaging inverse problems by integrating physical measurement models with pre-trained deep denoisers as priors. Score-based diffusion models (SBMs) have recently emerged as a powerful framew
Externí odkaz:
http://arxiv.org/abs/2412.11108
Total variation (TV) is a widely used function for regularizing imaging inverse problems that is particularly appropriate for images whose underlying structure is piecewise constant. TV regularized optimization problems are typically solved using pro
Externí odkaz:
http://arxiv.org/abs/2412.07718
Selecting an appropriate prior to compensate for information loss due to the measurement operator is a fundamental challenge in imaging inverse problems. Implicit priors based on denoising neural networks have become central to widely-used frameworks
Externí odkaz:
http://arxiv.org/abs/2411.18970
Autor:
Park, Chicago Y., McCann, Michael T., Garcia-Cardona, Cristina, Wohlberg, Brendt, Kamilov, Ulugbek S.
We present a simple template for designing generative diffusion model algorithms based on an interpretation of diffusion sampling as a sequence of random walks. Score-based diffusion models are widely used to generate high-quality images. Diffusion m
Externí odkaz:
http://arxiv.org/abs/2411.18702
Diffusion bridges (DB) have emerged as a promising alternative to diffusion models for imaging inverse problems, achieving faster sampling by directly bridging low- and high-quality image distributions. While incorporating measurement consistency has
Externí odkaz:
http://arxiv.org/abs/2411.16535
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
Diffusion models can generate a variety of high-quality images by modeling complex data distributions. Trained diffusion models can also be very effective image priors for solving inverse problems. Most of the existing diffusion-based methods integra
Externí odkaz:
http://arxiv.org/abs/2409.08906
Seismic data frequently exhibits missing traces, substantially affecting subsequent seismic processing and interpretation. Deep learning-based approaches have demonstrated significant advancements in reconstructing irregularly missing seismic data th
Externí odkaz:
http://arxiv.org/abs/2407.17402
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:
Xie, Huidong, Gan, Weijie, Zhou, Bo, Chen, Ming-Kai, Kulon, Michal, Boustani, Annemarie, Spencer, Benjamin A., Bayerlein, Reimund, Ji, Wei, Chen, Xiongchao, Liu, Qiong, Guo, Xueqi, Xia, Menghua, Zhou, Yinchi, Liu, Hui, Guo, Liang, An, Hongyu, Kamilov, Ulugbek S., Wang, Hanzhong, Li, Biao, Rominger, Axel, Shi, Kuangyu, Wang, Ge, Badawi, Ramsey D., Liu, Chi
Reducing scan times, radiation dose, and enhancing image quality, especially for lower-performance scanners, are critical in low-count/low-dose PET imaging. Deep learning (DL) techniques have been investigated for PET image denoising. However, existi
Externí odkaz:
http://arxiv.org/abs/2405.12996