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Found 6 papers, 4 papers with code
On Exact and Robust Recovery for Plug-and-Play Compressed Sensing
$Ax=A\xi$, where $A$ is a $m\times n$ random sensing matrix, $\Phi$ is the regularizer associated with a linear denoiser $W$, and $\xi$ is the ground-truth.
On Plug-and-Play Regularization using Linear Denoisers
This is typically done by taking a proximal algorithm such as FISTA or ADMM, and formally replacing the proximal map associated with a regularizer by nonlocal means, BM3D or a CNN denoiser.
Fixed-Point and Objective Convergence of Plug-and-Play Algorithms
A standard model for image reconstruction involves the minimization of a data-fidelity term along with a regularizer, where the optimization is performed using proximal algorithms such as ISTA and ADMM.
Plug-and-play ISTA converges with kernel denoisers
Plug-and-play (PnP) method is a recent paradigm for image regularization, where the proximal operator (associated with some given regularizer) in an iterative algorithm is replaced with a powerful denoiser.
On the Proof of Fixed-Point Convergence for Plug-and-Play ADMM
We argue that the original proof is incomplete, since convergence is not analyzed for one of the three possible cases outlined in the paper.
Fast Adaptive Bilateral Filtering
In the classical bilateral filter, a fixed Gaussian range kernel is used along with a spatial kernel for edge-preserving smoothing.
