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Found 6 papers, 4 papers with code
Convergent plug-and-play with proximal denoiser and unconstrained regularization parameter
The stepsize condition for nonconvex convergence of the proximal algorithm in use then translates to restrictive conditions on the regularization parameter of the inverse problem.
A relaxed proximal gradient descent algorithm for convergent plug-and-play with proximal denoiser
This paper presents a new convergent Plug-and-Play (PnP) algorithm.
An Analysis of Generative Methods for Multiple Image Inpainting
In this work, we focus on learning-based image completion methods for multiple and diverse inpainting which goal is to provide a set of distinct solutions for a given damaged image.
Proximal Denoiser for Convergent Plug-and-Play Optimization with Nonconvex Regularization
Given this new result, we exploit the convergence theory of proximal algorithms in the nonconvex setting to obtain convergence results for PnP-PGD (Proximal Gradient Descent) and PnP-ADMM (Alternating Direction Method of Multipliers).
Gradient Step Denoiser for convergent Plug-and-Play
Exploiting convergence results for proximal gradient descent algorithms in the non-convex setting, we show that the proposed Plug-and-Play algorithm is a convergent iterative scheme that targets stationary points of an explicit global functional.
Self-Supervised Small Soccer Player Detection and Tracking
In a soccer game, the information provided by detecting and tracking brings crucial clues to further analyze and understand some tactical aspects of the game, including individual and team actions.
