Results 21 to 30 of about 1,135 (244)
Deep plug-and-play prior for hyperspectral image restoration
Neurocomputing, 2022 code at https://github.com/Zeqiang-Lai ...
Zeqiang Lai, Kaixuan Wei, Ying Fu 0001
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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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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 +5 more
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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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Deep plug-and-play priors for spectral snapshot compressive imaging
, 2021 We propose a plug-and-play (PnP) method that uses deep-learning-based denoisers as regularization priors for spectral snapshot compressive imaging (SCI).
Han, Shensheng +7 more
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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
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Adaptive noise depression for functional brain network estimation
Frontiers in Psychiatry, 2023 Autism spectrum disorder (ASD) is one common psychiatric illness that manifests in neurological and developmental disorders, which can last throughout a person's life and cause challenges in social interaction, communication, and behavior.
Di Ma, Di Ma, Liling Peng, Xin Gao
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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
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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
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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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