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Found 7 papers, 4 papers with code
On the Contractivity of Plug-and-Play Operators
The present work is motivated by the following questions: Can we relax the assumptions on the forward model?
Guided Nonlocal Patch Regularization and Efficient Filtering-Based Inversion for Multiband Fusion
In multiband fusion, an image with a high spatial and low spectral resolution is combined with an image with a low spatial but high spectral resolution to produce a single multiband image having high spatial and spectral resolutions.
Plug-and-Play Regularization using Linear Solvers
Coupled with a model-based loss function, these are typically used for image reconstruction within an optimization framework.
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.
Averaged Deep Denoisers for Image Regularization
In this work, we construct contractive and averaged image denoisers by unfolding splitting-based optimization algorithms applied to wavelet denoising and demonstrate that their regularization capacity for PnP and RED can be matched with CNN denoisers.
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.
