Results 21 to 30 of about 1,202 (247)
Deep plug-and-play prior for low-rank tensor completion [PDF]
Neurocomputing, 2020 Multi-dimensional images, such as color images and multi-spectral images, are highly correlated and contain abundant spatial and spectral information. However, real-world multi-dimensional images are usually corrupted by missing entries. By integrating deterministic low-rankness prior to the data-driven deep prior, we suggest a novel regularized tensor
Xi-Le Zhao +4 more
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On Maximum-a-Posteriori estimation with Plug & Play priors and stochastic gradient descent [PDF]
, 2023 International audienceBayesian methods to solve imaging inverse problems usually combine an explicit data likelihood function with a prior distribution that explicitly models expected properties of the solution. Many kinds of priors have been explored in
Durmus, Alain +11 more
core +1 more source
Boosting the Performance of Plug-and-Play Priors via Denoiser Scaling [PDF]
2020 54th Asilomar Conference on Signals, Systems, and Computers, 2020 Plug-and-play priors (PnP) is an image reconstruction framework that uses an image denoiser as an imaging prior. Unlike traditional regularized inversion, PnP does not require the prior to be expressible in the form of a regularization function. This flexibility enables PnP algorithms to exploit the most effective image denoisers, leading to their ...
Xiaojian Xu 0002 +4 more
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Hyperspectral Anomaly Detection via Deep Plug-and-Play Denoising CNN Regularization
, 2021 Due to the importance in many military and civilian applications, hyperspectral anomaly detection has attracted remarkable interest. Low-rank representation (LRR)-based anomaly detectors use the low-rank property to represent background pixels, and ...
Jia, Sen +11 more
core +1 more source
Preconditioned Plug-and-Play ADMM with Locally Adjustable Denoiser for Image Restoration Mikael
, 2022 International audiencePlug-and-Play priors recently emerged as a powerful technique for solving inverse problems by plugging a denoiser into a classical optimization algorithm.
Guillemot, Christine, Le Pendu, Mikael
core +2 more sources
Deep Plug-and-Play Prior for Parallel MRI Reconstruction [PDF]
2019 IEEE/CVF International Conference on Computer Vision Workshop (ICCVW), 2019 Fast data acquisition in Magnetic Resonance Imaging (MRI) is vastly in demand and scan time directly depends on the number of acquired k-space samples. Conventional MRI reconstruction methods for fast MRI acquisition mostly relied on different regularizers which represent analytical models of sparsity.
Ali Pour Yazdanpanah +2 more
openaire +4 more sources
Multilevel Plug-and-Play Image Restoration
Plug-and-play (PnP) image reconstruction methods leverage pretrained deep neural network denoisers as image priors to solve general inverse problems, and can obtain a competitive performance without having to train a network on a specific problem ...
Riccietti, Elisa +3 more
core +9 more sources
Bioengineering, 2023 Magnetic Resonance Imaging (MRI) is an essential medical imaging modality that provides excellent soft-tissue contrast and high-resolution images of the human body, allowing us to understand detailed information on morphology, structural integrity, and ...
Dilbag Singh +5 more
doaj +1 more source
Regularized Fourier ptychography using an online plug-and-play algorithm [PDF]
, 2018 The plug-and-play priors (PnP) framework has been recently shown to achieve state-of-the-art results in regularized image reconstruction by leveraging a sophisticated denoiser within an iterative algorithm.
Sun, Yu +5 more
core +2 more sources
Plug-and-play priors for model based reconstruction [PDF]
, 2020 -Model-based reconstruction is a powerful framework for solving a variety of inverse problems in imaging. In recent years, enormous progress has been made in the problem of denoising, a special case of an inverse problem where the forward model is an ...
Brendt Wohlberg +2 more
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