1,457 research outputs found
Confidence driven TGV fusion
We introduce a novel model for spatially varying variational data fusion,
driven by point-wise confidence values. The proposed model allows for the joint
estimation of the data and the confidence values based on the spatial coherence
of the data. We discuss the main properties of the introduced model as well as
suitable algorithms for estimating the solution of the corresponding biconvex
minimization problem and their convergence. The performance of the proposed
model is evaluated considering the problem of depth image fusion by using both
synthetic and real data from publicly available datasets
Convergent plug-and-play with proximal denoiser and unconstrained regularization parameter
In this work, we present new proofs of convergence for Plug-and-Play (PnP)
algorithms. PnP methods are efficient iterative algorithms for solving image
inverse problems where regularization is performed by plugging a pre-trained
denoiser in a proximal algorithm, such as Proximal Gradient Descent (PGD) or
Douglas-Rachford Splitting (DRS). Recent research has explored convergence by
incorporating a denoiser that writes exactly as a proximal operator. However,
the corresponding PnP algorithm has then to be run with stepsize equal to .
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. This can severely degrade the restoration capacity of
the algorithm. In this paper, we present two remedies for this limitation.
First, we provide a novel convergence proof for PnP-DRS that does not impose
any restrictions on the regularization parameter. Second, we examine a relaxed
version of the PGD algorithm that converges across a broader range of
regularization parameters. Our experimental study, conducted on deblurring and
super-resolution experiments, demonstrate that both of these solutions enhance
the accuracy of image restoration.Comment: arXiv admin note: substantial text overlap with arXiv:2301.1373
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. PnP
methods are efficient iterative algorithms for solving image inverse problems
formulated as the minimization of the sum of a data-fidelity term and a
regularization term. PnP methods perform regularization by plugging a
pre-trained denoiser in a proximal algorithm, such as Proximal Gradient Descent
(PGD). To ensure convergence of PnP schemes, many works study specific
parametrizations of deep denoisers. However, existing results require either
unverifiable or suboptimal hypotheses on the denoiser, or assume restrictive
conditions on the parameters of the inverse problem. Observing that these
limitations can be due to the proximal algorithm in use, we study a relaxed
version of the PGD algorithm for minimizing the sum of a convex function and a
weakly convex one. When plugged with a relaxed proximal denoiser, we show that
the proposed PnP-PGD algorithm converges for a wider range of
regularization parameters, thus allowing more accurate image restoration
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