Results 11 to 20 of about 1,135 (244)
Face Restoration via Plug-and-Play 3D Facial Priors [PDF]
IEEE Transactions on Pattern Analysis and Machine Intelligence, 2022 State-of-the-art face restoration methods employ deep convolutional neural networks (CNNs) to learn a mapping between degraded and sharp facial patterns by exploring local appearance knowledge. However, most of these methods do not well exploit facial structures and identity information, and only deal with task-specific face restoration (e.g.,face ...
Xiaobin Hu +7 more
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Recovery Analysis for Plug-and-Play Priors using the Restricted Eigenvalue Condition [PDF]
CoRR, 2021 The plug-and-play priors (PnP) and regularization by denoising (RED) methods have become widely used for solving inverse problems by leveraging pre-trained deep denoisers as image priors. While the empirical imaging performance and the theoretical convergence properties of these algorithms have been widely investigated, their recovery properties have ...
Liu, Jiaming +3 more
core +6 more sources
A Plug-and-Play Deep Image Prior [PDF]
ICASSP 2021 - 2021 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), 2021 Deep image priors (DIP) offer a novel approach for the regularization that leverages the inductive bias of a deep convolutional architecture in inverse problems. However, the quality of DIP approaches often degrades when the number of iterations exceeds a certain threshold due to overfitting.
Zhaodong Sun +3 more
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Tuning-Free Plug-and-Play Hyperspectral Image Deconvolution With Deep Priors [PDF]
IEEE Transactions on Geoscience and Remote Sensing, 2023 IEEE Trans. Geosci. Remote sens., to be published. Manuscript submitted Jun. 30, 2022; revised Oct. 25, 2022, and Dec. 06, 2022; and accepted Feb.
Wang, Xiuheng +2 more
openaire +5 more sources
Joint Hyperspectral Image Deconvolution and Unmixing via Plug-and-Play Priors
Remote SensingHyperspectral imaging (HSI) provides rich spatial and spectral information for remote sensing, mineral exploration, and biomedical analysis, but its limited spatial resolution and sensor imperfections lead to blurred, noisy, and mixed-pixel observations.
Sina Layazali, Chrysanthe Preza
doaj +2 more sources
Dimensionality reduced plug and play priors for improving photoacoustic tomographic imaging with limited noisy data. [PDF]
Biomed Opt Express, 2021 The reconstruction methods for solving the ill-posed inverse problem of photoacoustic tomography with limited noisy data are iterative in nature to provide accurate solutions.
Awasthi N +3 more
europepmc +3 more sources
Plug-and-Play Image Restoration With Deep Denoiser Prior [PDF]
IEEE Transactions on Pattern Analysis and Machine Intelligence, 2022 An extended version of IRCNN (CVPR17).
Zhang, Kai +5 more
openaire +5 more sources
2022 IEEE International Conference on Image Processing (ICIP), 2022 The past few years have seen a surge of activity around integration of deep learning networks and optimization algorithms for solving inverse problems. Recent work on plug-and-play priors (PnP), regularization by denoising (RED), and deep unfolding has shown the state-of-the-art performance of such integration in a variety of applications. However, the
Abdullah H. Al-Shabili +3 more
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Diffusion Models as Plug-And-Play Priors
Advances in Neural Information Processing Systems 35, 2022 NeurIPS 2022; code: https://github.com/AlexGraikos ...
Alexandros Graikos +3 more
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Video Restoration with a Deep Plug-and-Play Prior
CoRR, 2022 This paper presents a novel method for restoring digital videos via a Deep Plug-and-Play (PnP) approach. Under a Bayesian formalism, the method consists in using a deep convolutional denoising network in place of the proximal operator of the prior in an alternating optimization scheme.
Antoine Monod +3 more
openaire +2 more sources