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.
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.
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.
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 • 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.
Ranked #1 on Video Frame Interpolation on Xiph 4k
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 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.
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 • 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.
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.
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 • 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 • 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.
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.
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 • 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 • 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.
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 • 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 • 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.
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 • 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 • 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 • 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 • 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 • 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 • 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.
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 • 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.
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 • 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.
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 • 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 • 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 • 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 • 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 • 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 • 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 • 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.
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 • 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.
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.
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 • 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.
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 • 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 • 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 • 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 • 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 • 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.