Search Results for author: Junqi Tang

Found 15 papers, 3 papers with code

Unsupervised approaches based on optimal transport and convex analysis for inverse problems in imaging

no code implementations15 Nov 2023 Marcello Carioni, Subhadip Mukherjee, Hong Ye Tan, Junqi Tang

Together with a detailed survey, we provide an overview of the key mathematical results that underlie the methods reviewed in the chapter to keep our discussion self-contained.

Deep Unrolling Networks with Recurrent Momentum Acceleration for Nonlinear Inverse Problems

1 code implementation30 Jul 2023 Qingping Zhou, Jiayu Qian, Junqi Tang, Jinglai Li

We provide experimental results on two nonlinear inverse problems: a nonlinear deconvolution problem, and an electrical impedance tomography problem with limited boundary measurements.

NF-ULA: Langevin Monte Carlo with Normalizing Flow Prior for Imaging Inverse Problems

1 code implementation17 Apr 2023 Ziruo Cai, Junqi Tang, Subhadip Mukherjee, Jinglai Li, Carola Bibiane Schönlieb, Xiaoqun Zhang

Bayesian methods for solving inverse problems are a powerful alternative to classical methods since the Bayesian approach offers the ability to quantify the uncertainty in the solution.

Bayesian Inference Computed Tomography (CT) +4

Provably Convergent Plug-and-Play Quasi-Newton Methods

1 code implementation9 Mar 2023 Hong Ye Tan, Subhadip Mukherjee, Junqi Tang, Carola-Bibiane Schönlieb

Plug-and-Play (PnP) methods are a class of efficient iterative methods that aim to combine data fidelity terms and deep denoisers using classical optimization algorithms, such as ISTA or ADMM, with applications in inverse problems and imaging.

Deblurring Image Deblurring +1

Accelerating Deep Unrolling Networks via Dimensionality Reduction

no code implementations31 Aug 2022 Junqi Tang, Subhadip Mukherjee, Carola-Bibiane Schönlieb

In this work we propose a new paradigm for designing efficient deep unrolling networks using dimensionality reduction schemes, including minibatch gradient approximation and operator sketching.

Dimensionality Reduction Image Reconstruction +1

Stochastic Primal-Dual Three Operator Splitting with Arbitrary Sampling and Preconditioning

no code implementations2 Aug 2022 Junqi Tang, Matthias Ehrhardt, Carola-Bibiane Schönlieb

In this work we propose a stochastic primal-dual preconditioned three-operator splitting algorithm for solving a class of convex three-composite optimization problems.

Operator Sketching for Deep Unrolling Networks

no code implementations21 Mar 2022 Junqi Tang, Subhadip Mukherjee, Carola-Bibiane Schönlieb

In this work we propose a new paradigm for designing efficient deep unrolling networks using operator sketching.

Image Reconstruction Rolling Shutter Correction

Accelerating Plug-and-Play Image Reconstruction via Multi-Stage Sketched Gradients

no code implementations14 Mar 2022 Junqi Tang

Unlike existing approaches which utilize stochastic gradient iterations for acceleration, we propose novel multi-stage sketched gradient iterations which first perform downsampling dimensionality reduction in the image space, and then efficiently approximate the true gradient using the sketched gradient in the low-dimensional space.

Dimensionality Reduction Image Reconstruction

Data-Consistent Local Superresolution for Medical Imaging

no code implementations22 Feb 2022 Junqi Tang

This algorithmic framework is tailor for a clinical need in medical imaging practice, that after a reconstruction of the full tomographic image, the clinician may believe that some critical parts of the image are not clear enough, and may wish to see clearer these regions-of-interest.

Equivariance Regularization for Image Reconstruction

no code implementations10 Feb 2022 Junqi Tang

In this work, we propose Regularization-by-Equivariance (REV), a novel structure-adaptive regularization scheme for solving imaging inverse problems under incomplete measurements.

Image Reconstruction

Stochastic Primal-Dual Deep Unrolling

no code implementations19 Oct 2021 Junqi Tang, Subhadip Mukherjee, Carola-Bibiane Schönlieb

We develop a stochastic (ordered-subsets) variant of the classical learned primal-dual (LPD), which is a state-of-the-art unrolling network for tomographic image reconstruction.

Computational Efficiency Computed Tomography (CT) +2

A Fast Stochastic Plug-and-Play ADMM for Imaging Inverse Problems

no code implementations20 Jun 2020 Junqi Tang, Mike Davies

In this work we propose an efficient stochastic plug-and-play (PnP) algorithm for imaging inverse problems.

The Neural Tangent Link Between CNN Denoisers and Non-Local Filters

no code implementations CVPR 2021 Julián Tachella, Junqi Tang, Mike Davies

While the NTK theory accurately predicts the filter associated with networks trained using standard gradient descent, our analysis shows that it falls short to explain the behaviour of networks trained using the popular Adam optimizer.

Image Denoising Image Restoration

SPRING: A fast stochastic proximal alternating method for non-smooth non-convex optimization

no code implementations27 Feb 2020 Derek Driggs, Junqi Tang, Jingwei Liang, Mike Davies, Carola-Bibiane Schönlieb

We introduce SPRING, a novel stochastic proximal alternating linearized minimization algorithm for solving a class of non-smooth and non-convex optimization problems.

Image Deconvolution Stochastic Optimization Optimization and Control 90C26

The Practicality of Stochastic Optimization in Imaging Inverse Problems

no code implementations22 Oct 2019 Junqi Tang, Karen Egiazarian, Mohammad Golbabaee, Mike Davies

We investigate this phenomenon and propose a theory-inspired mechanism for the practitioners to efficiently characterize whether it is beneficial for an inverse problem to be solved by stochastic optimization techniques or not.

Deblurring Image Deblurring +1

Cannot find the paper you are looking for? You can Submit a new open access paper.