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20182022
most citedProvable Convergence of Plug-and-Play Priors with MMSE denoisers

56 citations · 89 across the 7 of their papers we have counts for

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6 papers · 1 filter

cs.CV2019

Block Coordinate Regularization by Denoising

Yu Sun, Jiaming Liu, Ulugbek S. Kamilov

We consider the problem of estimating a vector from its noisy measurements using a prior specified only through a denoising function. Recent work on plug-and-play priors (PnP) and…

cs.CV2018

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

Yu Sun, Shiqi Xu, Yunzhe Li +3

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 with…

cs.CV2018

Image Restoration using Total Variation Regularized Deep Image Prior

Jiaming Liu, Yu Sun, Xiaojian Xu +1

In the past decade, sparsity-driven regularization has led to significant improvements in image reconstruction. Traditional regularizers, such as total variation (TV), rely on anal…

cs.CV2018

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

Yu Sun, Brendt Wohlberg, Ulugbek S. Kamilov

Plug-and-play priors (PnP) is a powerful framework for regularizing imaging inverse problems by using advanced denoisers within an iterative algorithm. Recent experimental evidence…

cs.CV2018

Stability of Scattering Decoder For Nonlinear Diffractive Imaging

Yu Sun, Ulugbek S. Kamilov

The problem of image reconstruction under multiple light scattering is usually formulated as a regularized non-convex optimization. A deep learning architecture, Scattering Decoder…

cs.CV2018

Efficient and accurate inversion of multiple scattering with deep learning

Yu Sun, Zhihao Xia, Ulugbek S. Kamilov

Image reconstruction under multiple light scattering is crucial in a number of applications such as diffraction tomography. The reconstruction problem is often formulated as a nonc…