33 citations · 92 across the 19 of their papers we have counts for
4 papers · 1 filter
Practical Operator Sketching Framework for Accelerating Iterative Data-Driven Solutions in Inverse Problems
Junqi Tang, Guixian Xu, Subhadip Mukherjee +1
We propose a new operator-sketching paradigm for designing efficient iterative data-driven reconstruction (IDR) schemes, e.g. Plug-and-Play algorithms and deep unrolling networks.…
Accelerating Plug-and-Play Image Reconstruction via Multi-Stage Sketched Gradients
Junqi Tang
In this work we propose a new paradigm for designing fast plug-and-play (PnP) algorithms using dimensionality reduction techniques. Unlike existing approaches which utilize stochas…
Fast Gradient Methods for Data-Consistent Local Super-Resolution of Medical Images
Junqi Tang, Guixian Xu, Jinglai Li
In this work, we propose a new paradigm of iterative model-based reconstruction algorithms for providing real-time solution for zooming-in and refining a region of interest in medi…
Stochastic Primal-Dual Deep Unrolling
Junqi Tang, Subhadip Mukherjee, Carola-Bibiane Schönlieb
We propose a new type of efficient deep-unrolling networks for solving imaging inverse problems. Conventional deep-unrolling methods require full forward operator and its adjoint a…