Search Results for author: Ulugbek S. Kamilov

Found 28 papers, 5 papers with code

Learning-based Motion Artifact Removal Networks (LEARN) for Quantitative $R_2^\ast$ Mapping

no code implementations3 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.

Deformation-Compensated Learning for Image Reconstruction without Ground Truth

no code implementations12 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.

Image Reconstruction

Recovery Analysis for Plug-and-Play Priors using the Restricted Eigenvalue Condition

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.

Compressive Sensing Denoising

SGD-Net: Efficient Model-Based Deep Learning with Theoretical Guarantees

1 code implementation22 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.

Joint Reconstruction and Calibration using Regularization by Denoising

no code implementations26 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.

Denoising Image Reconstruction

Async-RED: A Provably Convergent Asynchronous Block Parallel Stochastic Method using Deep Denoising Priors

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.

Denoising

Deep Image Reconstruction using Unregistered Measurements without Groundtruth

no code implementations29 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.

Image Reconstruction

Scalable Plug-and-Play ADMM with Convergence Guarantees

no code implementations5 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.

Provable Convergence of Plug-and-Play Priors with MMSE denoisers

no code implementations15 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.

Compressive Sensing Image Reconstruction

Infusing Learned Priors into Model-Based Multispectral Imaging

no code implementations20 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.

Denoising Image Reconstruction

Online Regularization by Denoising with Applications to Phase Retrieval

no code implementations4 Sep 2019 Zihui Wu, Yu Sun, Jiaming Liu, Ulugbek S. Kamilov

Regularization by denoising (RED) is a powerful framework for solving imaging inverse problems.

Denoising

Block Coordinate Regularization by Denoising

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.

Denoising

Plug-In Stochastic Gradient Method

no code implementations8 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.

Regularized Fourier Ptychography using an Online Plug-and-Play Algorithm

no code implementations31 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.

Image Reconstruction

Image Restoration using Total Variation Regularized Deep Image Prior

no code implementations30 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.

Deblurring Image Denoising +2

An Online Plug-and-Play Algorithm for Regularized Image Reconstruction

1 code implementation12 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.

Image Reconstruction

signProx: One-Bit Proximal Algorithm for Nonconvex Stochastic Optimization

no code implementations20 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.

Stochastic Optimization

Stability of Scattering Decoder For Nonlinear Diffractive Imaging

no code implementations20 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.

Image Reconstruction

Sparse Blind Deconvolution for Distributed Radar Autofocus Imaging

no code implementations8 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.

Efficient and accurate inversion of multiple scattering with deep learning

3 code implementations18 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.

Image Reconstruction

Learning-based Image Reconstruction via Parallel Proximal Algorithm

no code implementations29 Jan 2018 Emrah Bostan, Ulugbek S. Kamilov, Laura Waller

In the past decade, sparsity-driven regularization has led to advancement of image reconstruction algorithms.

Image Reconstruction

Accelerated Image Reconstruction for Nonlinear Diffractive Imaging

no code implementations4 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.

Image Reconstruction

Online Convolutional Dictionary Learning for Multimodal Imaging

no code implementations13 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.

Dictionary Learning

Compressive Imaging with Iterative Forward Models

no code implementations5 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.

A Recursive Born Approach to Nonlinear Inverse Scattering

no code implementations11 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.

Transparent objects

Depth Superresolution using Motion Adaptive Regularization

no code implementations4 Mar 2016 Ulugbek S. Kamilov, Petros T. Boufounos

Spatial resolution of depth sensors is often significantly lower compared to that of conventional optical cameras.

Learning optimal nonlinearities for iterative thresholding algorithms

no code implementations15 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.

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