Unfolded proximal neural networks for robust image Gaussian denoising
A common approach to solve inverse imaging problems relies on finding a maximum a
posteriori (MAP) estimate of the original unknown image, by solving a minimization problem …
posteriori (MAP) estimate of the original unknown image, by solving a minimization problem …
Plug-and-play image reconstruction is a convergent regularization method
A Ebner, M Haltmeier - IEEE Transactions on Image Processing, 2024 - ieeexplore.ieee.org
Non-uniqueness and instability are characteristic features of image reconstruction methods.
As a result, it is necessary to develop regularization methods that can be used to compute …
As a result, it is necessary to develop regularization methods that can be used to compute …
Averaged Deep Denoisers for Image Regularization
P Nair, KN Chaudhury - Journal of Mathematical Imaging and Vision, 2024 - Springer
Abstract Plug-and-Play (PnP) and Regularization-by-Denoising (RED) are recent paradigms
for image reconstruction that leverage the power of modern denoisers for image …
for image reconstruction that leverage the power of modern denoisers for image …
Analysis and synthesis denoisers for forward-backward plug-and-play algorithms
M Kowalski, B Malézieux, T Moreau… - arxiv preprint arxiv …, 2024 - arxiv.org
In this work we study the behavior of the forward-backward (FB) algorithm when the
proximity operator is replaced by a sub-iterative procedure to approximate a Gaussian …
proximity operator is replaced by a sub-iterative procedure to approximate a Gaussian …
PNN: From proximal algorithms to robust unfolded image denoising networks and Plug-and-Play methods
A common approach to solve inverse imaging problems relies on finding a maximum a
posteriori (MAP) estimate of the original unknown image, by solving a minimization problem …
posteriori (MAP) estimate of the original unknown image, by solving a minimization problem …
A data-driven approach for bayesian uncertainty quantification in imaging
M Tang, A Repetti - arxiv preprint arxiv:2304.11200, 2023 - arxiv.org
Uncertainty quantification in image restoration is a prominent challenge, mainly due to the
high dimensionality of the encountered problems. Recently, a Bayesian uncertainty …
high dimensionality of the encountered problems. Recently, a Bayesian uncertainty …
A deep encoder-decoder based primal-dual proximal network for image restoration
S Wang, M Jiu, L Chen, S Li… - … Conference on Graphics …, 2024 - spiedigitallibrary.org
Image restoration is a popular and challenge task, which is regarded as a classical inverse
problem. Condat-V ũ primal-dual algorithm based on proximal operator is one of successful …
problem. Condat-V ũ primal-dual algorithm based on proximal operator is one of successful …
A lifted Bregman strategy for training unfolded proximal neural network Gaussian denoisers
Unfolded proximal neural networks (PNNs) form a family of methods that combines deep
learning and proximal optimization approaches. They consist in designing a neural network …
learning and proximal optimization approaches. They consist in designing a neural network …
Plug-and-play imaging with model uncertainty quantification in radio astronomy
Plug-and-Play (PnP) algorithms are appealing alternatives to proximal algorithms when
solving inverse imaging problems. By learning a Deep Neural Network (DNN) behaving as a …
solving inverse imaging problems. By learning a Deep Neural Network (DNN) behaving as a …
PNN: From proximal algorithms to robust unfolded image denoising networks and Plug-and-Play methods
H Trieu, A Repetti, N Pustelnik - IEEE Transactions on Image …, 2024 - hal.science
A common approach to solve inverse imaging problems relies on finding a maximum a
posteriori (MAP) estimate of the original unknown image, by solving a minimization problem …
posteriori (MAP) estimate of the original unknown image, by solving a minimization problem …