5 citations · 5 across the 7 of their papers we have counts for
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Domain Adaptation of Mismatched Proximal Denoiser for Plug-and-Play Image Reconstruction
Guixian Xu, Jinglai Li, Junqi Tang
Plug-and-play proximal gradient descent (PnP-PGD) enables flexible image reconstruction by using denoisers as implicit priors. In practice, these denoisers are often deployed outsi…
Fast Equivariant Imaging: Accelerating Unsupervised Learning and Model Adaptation via Inexact Splitting
Guixian Xu, Jinglai Li, Junqi Tang
In this work, we propose Fast Equivariant Imaging (FEI), a novel unsupervised learning framework to rapidly and efficiently train deep imaging networks without ground-truth data. F…
Equivariant Test-Time Training with Operator Sketching for Imaging Inverse Problems
Guixian Xu, Jinglai Li, Junqi Tang
Equivariant Imaging (EI) regularization has become the de-facto technique for unsupervised training of deep imaging networks, without any need of ground-truth data. Observing that…
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.…
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…