no code implementations • 6 Jan 2025 • Harry Gao, Weijie Gan, Yuyang Hu, Hongyu An, Ulugbek S. Kamilov
Diffusion bridges (DBs) are a class of diffusion models that enable faster sampling by interpolating between two paired image distributions.
no code implementations • 21 Dec 2024 • Huidong Xie, Weijie Gan, Wei Ji, Xiongchao Chen, Alaa Alashi, Stephanie L. Thorn, Bo Zhou, Qiong Liu, Menghua Xia, Xueqi Guo, Yi-Hwa Liu, Hongyu An, Ulugbek S. Kamilov, Ge Wang, Albert J. Sinusas, Chi Liu
This work introduced DiffSPECT-3D, a diffusion framework for 3D cardiac SPECT imaging that effectively adapts to different acquisition settings without requiring further network re-training or fine-tuning.
1 code implementation • 15 Dec 2024 • Chicago Y. Park, Yuyang Hu, Michael T. McCann, Cristina Garcia-Cardona, Brendt Wohlberg, Ulugbek S. Kamilov
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.
no code implementations • 10 Dec 2024 • Edward P. Chandler, Shirin Shoushtari, Brendt Wohlberg, Ulugbek S. Kamilov
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.
no code implementations • 28 Nov 2024 • Matthieu Terris, Ulugbek S. Kamilov, Thomas Moreau
In this work, we introduce Fixed-points of Restoration (FiRe) priors as a new framework for expanding the notion of priors in PnP to general restoration models beyond traditional denoising models.
1 code implementation • 27 Nov 2024 • Chicago Y. Park, Michael T. McCann, Cristina Garcia-Cardona, Brendt Wohlberg, Ulugbek S. Kamilov
In particular, these templates cleanly separate training from sampling, e. g., the noise schedule used during training need not match the one used during sampling.
no code implementations • 25 Nov 2024 • Yuyang Hu, Albert Peng, Weijie Gan, Ulugbek S. Kamilov
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.
no code implementations • 2 Oct 2024 • Yuyang Hu, Albert Peng, Weijie Gan, Peyman Milanfar, Mauricio Delbracio, Ulugbek S. Kamilov
We introduce Stochastic deep Restoration Priors (ShaRP), a novel method that leverages an ensemble of such restoration models to regularize inverse problems.
no code implementations • 13 Sep 2024 • Nebiyou Yismaw, Ulugbek S. Kamilov, M. Salman Asif
Furthermore, we present an efficient way to factorize and invert the covariance matrix of the likelihood function for several inverse problems.
no code implementations • 17 May 2024 • Zihao Zou, Shirin Shoushtari, Jiaming Liu, Jialiang Zhang, Patrick Judge, Emilia Santana, Alison Lim, Marcus Foston, Ulugbek S. Kamilov
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.
no code implementations • 2 May 2024 • Huidong Xie, Weijie Gan, Bo Zhou, Ming-Kai Chen, Michal Kulon, Annemarie Boustani, Benjamin A. Spencer, Reimund Bayerlein, Wei Ji, Xiongchao Chen, Qiong Liu, Xueqi Guo, Menghua Xia, Yinchi Zhou, Hui Liu, Liang Guo, Hongyu An, Ulugbek S. Kamilov, Hanzhong Wang, Biao Li, Axel Rominger, Kuangyu Shi, Ge Wang, Ramsey D. Badawi, Chi Liu
However, existing models have often resulted in compromised image quality when achieving low-dose PET and have limited generalizability to different image noise-levels, acquisition protocols, and patient populations.
no code implementations • 26 Mar 2024 • Weijie Gan, Huidong Xie, Carl von Gall, Günther Platsch, Michael T. Jurkiewicz, Andrea Andrade, Udunna C. Anazodo, Ulugbek S. Kamilov, Hongyu An, Jorge Cabello
Anatomically guided PET reconstruction using MRI information has been shown to have the potential to improve PET image quality.
no code implementations • 15 Mar 2024 • Shirin Shoushtari, Edward P. Chandler, Jialiang Zhang, Manjula Senanayake, Sai Venkatesh Pingali, Marcus Foston, Ulugbek S. Kamilov
The prior in PR-SANS is initially trained on a set of generic images and subsequently fine-tuned using a limited amount of experimental SANS data.
no code implementations • 15 Mar 2024 • Edward P. Chandler, Shirin Shoushtari, Jiaming Liu, M. Salman Asif, Ulugbek S. Kamilov
A common issue with the learned models is that of a performance drop when there is a distribution shift between the training and testing data.
no code implementations • 30 Nov 2023 • Chicago Park, Shirin Shoushtari, Weijie Gan, Ulugbek S. Kamilov
This paper presents a theoretical explanation for the observed stability of PnP-ADMM based on the interpretation of the CNN prior as a minimum mean-squared error (MMSE) denoiser.
no code implementations • 29 Nov 2023 • Yuyang Hu, Satya V. V. N. Kothapalli, Weijie Gan, Alexander L. Sukstanskii, Gregory F. Wu, Manu Goyal, Dmitriy A. Yablonskiy, Ulugbek S. Kamilov
We introduce a new framework called DiffGEPCI for cross-modality generation in magnetic resonance imaging (MRI) using a 2. 5D conditional diffusion model.
no code implementations • 26 Nov 2023 • Zihao Zou, Jiaming Liu, Shirin Shoushtari, YuBo Wang, Weijie Gan, Ulugbek S. Kamilov
Face video restoration (FVR) is a challenging but important problem where one seeks to recover a perceptually realistic face videos from a low-quality input.
no code implementations • 7 Nov 2023 • Huidong Xie, Weijie Gan, Bo Zhou, Xiongchao Chen, Qiong Liu, Xueqi Guo, Liang Guo, Hongyu An, Ulugbek S. Kamilov, Ge Wang, Chi Liu
We extensively evaluated DDPET-3D on 100 patients with 6 different low-dose levels (a total of 600 testing studies), and demonstrated superior performance over previous diffusion models for 3D imaging problems as well as previous noise-aware medical image denoising models.
no code implementations • 3 Nov 2023 • Chicago Park, Weijie Gan, Zihao Zou, Yuyang Hu, Zhixin Sun, Ulugbek S. Kamilov
There is a growing interest in model-based deep learning (MBDL) for solving imaging inverse problems.
1 code implementation • 11 Oct 2023 • Weijie Gan, Qiuchen Zhai, Michael Thompson McCann, Cristina Garcia Cardona, Ulugbek S. Kamilov, Brendt Wohlberg
Ptychography is an imaging technique that captures multiple overlapping snapshots of a sample, illuminated coherently by a moving localized probe.
no code implementations • 10 Oct 2023 • Nebiyou Yismaw, Ulugbek S. Kamilov, M. Salman Asif
Deep learning-based methods deliver state-of-the-art performance for solving inverse problems that arise in computational imaging.
no code implementations • 6 Oct 2023 • Junhao Hu, Weijie Gan, Zhixin Sun, Hongyu An, Ulugbek S. Kamilov
A traditional DL approach to DIR is based on training a convolutional neural network (CNN) to estimate the registration field between two input images.
1 code implementation • 5 Oct 2023 • Marien Renaud, Jiaming Liu, Valentin De Bortoli, Andrés Almansa, Ulugbek S. Kamilov
Posterior sampling has been shown to be a powerful Bayesian approach for solving imaging inverse problems.
no code implementations • 2 Oct 2023 • Yuyang Hu, Mauricio Delbracio, Peyman Milanfar, Ulugbek S. Kamilov
Image denoisers have been shown to be powerful priors for solving inverse problems in imaging.
no code implementations • 29 Sep 2023 • Shirin Shoushtari, Jiaming Liu, Edward P. Chandler, M. Salman Asif, Ulugbek S. Kamilov
Our second set of numerical results considers a simple and effective domain adaption strategy that closes the performance gap due to the use of mismatched denoisers.
1 code implementation • 9 Mar 2023 • Zihao Zou, Jiaming Liu, Brendt Wohlberg, Ulugbek S. Kamilov
ELDER is based on a regularization functional parameterized by a CNN and a deep equilibrium learning (DEQ) method for training the functional to be MSE-optimal at the fixed points of the reconstruction algorithm.
no code implementations • ICCV 2023 • Jiaming Liu, Rushil Anirudh, Jayaraman J. Thiagarajan, Stewart He, K. Aditya Mohan, Ulugbek S. Kamilov, Hyojin Kim
Limited-Angle Computed Tomography (LACT) is a non-destructive evaluation technique used in a variety of applications ranging from security to medicine.
no code implementations • 1 Nov 2022 • Junhao Hu, Shirin Shoushtari, Zihao Zou, Jiaming Liu, Zhixin Sun, Ulugbek S. Kamilov
Deep model-based architectures (DMBAs) are widely used in imaging inverse problems to integrate physical measurement models and learned image priors.
no code implementations • 1 Nov 2022 • Shirin Shoushtari, Jiaming Liu, Ulugbek S. Kamilov
Phase retrieval refers to the problem of recovering an image from the magnitudes of its complex-valued linear measurements.
no code implementations • 26 Oct 2022 • Harry Gao, Weijie Gan, Zhixin Sun, Ulugbek S. Kamilov
Implicit neural representations (INR) have been recently proposed as deep learning (DL) based solutions for image compression.
no code implementations • 12 Oct 2022 • Xiaojian Xu, Weijie Gan, Satya V. V. N. Kothapalli, Dmitriy A. Yablonskiy, Ulugbek S. Kamilov
Quantitative MRI (qMRI) refers to a class of MRI methods for quantifying the spatial distribution of biological tissue parameters.
no code implementations • 7 Oct 2022 • Weijie Gan, Chunwei Ying, Parna Eshraghi, Tongyao Wang, Cihat Eldeniz, Yuyang Hu, Jiaming Liu, Yasheng Chen, Hongyu An, Ulugbek S. Kamilov
Our numerical results on in-vivo MRI data show that SelfDEQ leads to state-of-the-art performance using only undersampled and noisy training data.
no code implementations • 5 Oct 2022 • Yuyang Hu, Weijie Gan, Chunwei Ying, Tongyao Wang, Cihat Eldeniz, Jiaming Liu, Yasheng Chen, Hongyu An, Ulugbek S. Kamilov
However, estimation of accurate CSMs is a challenging problem when measurements are highly undersampled.
no code implementations • 23 Sep 2022 • Tomas Kerepecky, Jiaming Liu, Xue Wen Ng, David W. Piston, Ulugbek S. Kamilov
Three-dimensional fluorescence microscopy often suffers from anisotropy, where the resolution along the axial direction is lower than that within the lateral imaging plane.
no code implementations • 26 Jul 2022 • Shirin Shoushtari, Jiaming Liu, Yuyang Hu, Ulugbek S. Kamilov
While the empirical performance and theoretical properties of DMBAs have been widely investigated, the existing work in the area has primarily focused on their performance when the desired image prior is known exactly.
1 code implementation • 25 May 2022 • Jiaming Liu, Xiaojian Xu, Weijie Gan, Shirin Shoushtari, Ulugbek S. Kamilov
However, the dependence of the computational/memory complexity of the measurement models in PnP/RED on the total number of measurements leaves DEQ impractical for many imaging applications.
no code implementations • 10 Apr 2022 • Weijie Gan, Cihat Eldeniz, Jiaming Liu, Sihao Chen, Hongyu An, Ulugbek S. Kamilov
We propose a new plug-and-play priors (PnP) based MR image reconstruction method that systematically enforces data consistency while also exploiting deep-learning priors.
no code implementations • 31 Mar 2022 • Ulugbek S. Kamilov, Charles A. Bouman, Gregery T. Buzzard, Brendt Wohlberg
Plug-and-Play Priors (PnP) is one of the most widely-used frameworks for solving computational imaging problems through the integration of physical models and learned models.
1 code implementation • 28 Feb 2022 • Wentao Shangguan, Yu Sun, Weijie Gan, Ulugbek S. Kamilov
This paper considers the problem of temporal video interpolation, where the goal is to synthesize a new video frame given its two neighbors.
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no code implementations • 10 Feb 2022 • Yuyang Hu, Jiaming Liu, Xiaojian Xu, Ulugbek S. Kamilov
Regularization by denoising (RED) is a widely-used framework for solving inverse problems by leveraging image denoisers as image priors.
no code implementations • 4 Feb 2022 • Abdullah H. Al-Shabili, Xiaojian Xu, Ivan Selesnick, Ulugbek S. Kamilov
Our new Bregman Proximal Gradient Method variant of PnP (PnP-BPGM) and Bregman Steepest Descent variant of RED (RED-BSD) replace the traditional updates in PnP and RED from the quadratic norms to more general Bregman distance.
1 code implementation • 3 Sep 2021 • Xiaojian Xu, Satya V. V. N. Kothapalli, Jiaming Liu, Sayan Kahali, Weijie Gan, Dmitriy A. Yablonskiy, Ulugbek S. Kamilov
LEARN-IMG performs motion correction on mGRE images and relies on the subsequent analysis for the estimation of $R_2^\ast$ maps, while LEARN-BIO directly performs motion- and $B0$-inhomogeneity-corrected $R_2^\ast$ estimation.
1 code implementation • 12 Jul 2021 • Weijie Gan, Yu Sun, Cihat Eldeniz, Jiaming Liu, Hongyu An, Ulugbek S. Kamilov
Deep neural networks for medical image reconstruction are traditionally trained using high-quality ground-truth images as training targets.
1 code implementation • NeurIPS 2021 • Jiaming Liu, M. Salman Asif, Brendt Wohlberg, Ulugbek S. Kamilov
The plug-and-play priors (PnP) and regularization by denoising (RED) methods have become widely used for solving inverse problems by leveraging pre-trained deep denoisers as image priors.
1 code implementation • 9 Feb 2021 • Yu Sun, Jiaming Liu, Mingyang Xie, Brendt Wohlberg, Ulugbek S. Kamilov
We propose Coordinate-based Internal Learning (CoIL) as a new deep-learning (DL) methodology for the continuous representation of measurements.
1 code implementation • 22 Jan 2021 • Jiaming Liu, Yu Sun, Weijie Gan, Xiaojian Xu, Brendt Wohlberg, Ulugbek S. Kamilov
Deep unfolding networks have recently gained popularity in the context of solving imaging inverse problems.
no code implementations • 26 Nov 2020 • Mingyang Xie, Yu Sun, Jiaming Liu, Brendt Wohlberg, Ulugbek S. Kamilov
Cal-RED extends the traditional RED methodology to imaging problems that require the calibration of the measurement operator.
no code implementations • ICLR 2021 • Yu Sun, Jiaming Liu, Yiran Sun, Brendt Wohlberg, Ulugbek S. Kamilov
Regularization by denoising (RED) is a recently developed framework for solving inverse problems by integrating advanced denoisers as image priors.
no code implementations • 29 Sep 2020 • Weijie Gan, Yu Sun, Cihat Eldeniz, Jiaming Liu, Hongyu An, Ulugbek S. Kamilov
One of the key limitations in conventional deep learning based image reconstruction is the need for registered pairs of training images containing a set of high-quality groundtruth images.
no code implementations • 5 Jun 2020 • Yu Sun, Zihui Wu, Xiaojian Xu, Brendt Wohlberg, Ulugbek S. Kamilov
Plug-and-play priors (PnP) is a broadly applicable methodology for solving inverse problems by exploiting statistical priors specified as denoisers.
no code implementations • 15 May 2020 • Xiaojian Xu, Yu Sun, Jiaming Liu, Brendt Wohlberg, Ulugbek S. Kamilov
Plug-and-play priors (PnP) is a methodology for regularized image reconstruction that specifies the prior through an image denoiser.
no code implementations • 20 Sep 2019 • Jiaming Liu, Yu Sun, Ulugbek S. Kamilov
We introduce a new algorithm for regularized reconstruction of multispectral (MS) images from noisy linear measurements.
no code implementations • 4 Sep 2019 • Zihui Wu, Yu Sun, Jiaming Liu, Ulugbek S. Kamilov
Regularization by denoising (RED) is a powerful framework for solving imaging inverse problems.
1 code implementation • NeurIPS 2019 • Yu Sun, Jiaming Liu, Ulugbek S. Kamilov
In this work, we develop a new block coordinate RED algorithm that decomposes a large-scale estimation problem into a sequence of updates over a small subset of the unknown variables.
no code implementations • 8 Nov 2018 • Yu Sun, Brendt Wohlberg, Ulugbek S. Kamilov
Plug-and-play priors (PnP) is a popular framework for regularized signal reconstruction by using advanced denoisers within an iterative algorithm.
no code implementations • 31 Oct 2018 • Yu Sun, Shiqi Xu, Yunzhe Li, Lei Tian, Brendt Wohlberg, Ulugbek S. Kamilov
The plug-and-play priors (PnP) framework has been recently shown to achieve state-of-the-art results in regularized image reconstruction by leveraging a sophisticated denoiser within an iterative algorithm.
no code implementations • 30 Oct 2018 • Jiaming Liu, Yu Sun, Xiaojian Xu, Ulugbek S. Kamilov
In the past decade, sparsity-driven regularization has led to significant improvements in image reconstruction.
1 code implementation • 12 Sep 2018 • Yu Sun, Brendt Wohlberg, Ulugbek S. Kamilov
The results in this paper have the potential to expand the applicability of the PnP framework to very large and redundant datasets.
no code implementations • 20 Jul 2018 • Xiaojian Xu, Ulugbek S. Kamilov
Stochastic gradient descent (SGD) is one of the most widely used optimization methods for parallel and distributed processing of large datasets.
no code implementations • 20 Jun 2018 • Yu Sun, Ulugbek S. Kamilov
The problem of image reconstruction under multiple light scattering is usually formulated as a regularized non-convex optimization.
no code implementations • 8 May 2018 • Hassan Mansour, Dehong Liu, Ulugbek S. Kamilov, Petros T. Boufounos
Common techniques that attempt to resolve the antenna ambiguity generally assume an unknown gain and phase error afflicting the radar measurements.
4 code implementations • 18 Mar 2018 • Yu Sun, Zhihao Xia, Ulugbek S. Kamilov
Image reconstruction under multiple light scattering is crucial in a number of applications such as diffraction tomography.
no code implementations • 29 Jan 2018 • Emrah Bostan, Ulugbek S. Kamilov, Laura Waller
In the past decade, sparsity-driven regularization has led to advancement of image reconstruction algorithms.
no code implementations • 4 Aug 2017 • Yanting Ma, Hassan Mansour, Dehong Liu, Petros T. Boufounos, Ulugbek S. Kamilov
The problem of reconstructing an object from the measurements of the light it scatters is common in numerous imaging applications.
no code implementations • 13 Jun 2017 • Kevin Degraux, Ulugbek S. Kamilov, Petros T. Boufounos, Dehong Liu
Computational imaging methods that can exploit multiple modalities have the potential to enhance the capabilities of traditional sensing systems.
no code implementations • 5 May 2017 • Hsiou-Yuan Liu, Dehong Liu, Hassan Mansour, Petros T. Boufounos, Laura Waller, Ulugbek S. Kamilov
Specifically, it corresponds to a series expansion of the scattered wave with an accelerated-gradient method.
no code implementations • 5 Oct 2016 • Hsiou-Yuan Liu, Ulugbek S. Kamilov, Dehong Liu, Hassan Mansour, Petros T. Boufounos
We propose a new compressive imaging method for reconstructing 2D or 3D objects from their scattered wave-field measurements.
no code implementations • 11 Mar 2016 • Ulugbek S. Kamilov, Dehong Liu, Hassan Mansour, Petros T. Boufounos
The Iterative Born Approximation (IBA) is a well-known method for describing waves scattered by semi-transparent objects.
no code implementations • 4 Mar 2016 • Ulugbek S. Kamilov, Petros T. Boufounos
Spatial resolution of depth sensors is often significantly lower compared to that of conventional optical cameras.
no code implementations • 15 Dec 2015 • Ulugbek S. Kamilov, Hassan Mansour
Iterative shrinkage/thresholding algorithm (ISTA) is a well-studied method for finding sparse solutions to ill-posed inverse problems.