Plug-and-play methods for integrating physical and learned models in computational imaging: Theory, algorithms, and applications
Plug-and-play (PnP) priors constitute one of the most widely used frameworks for solving
computational imaging problems through the integration of physical models and learned …
computational imaging problems through the integration of physical models and learned …
Deep internal learning: Deep learning from a single input
Deep learning, in general, focuses on training a neural network from large labeled datasets.
Yet, in many cases, there is value in training a network just from the input at hand. This is …
Yet, in many cases, there is value in training a network just from the input at hand. This is …
Image restoration by denoising diffusion models with iteratively preconditioned guidance
Training deep neural networks has become a common approach for addressing image
restoration problems. An alternative for training a" task-specific" network for each …
restoration problems. An alternative for training a" task-specific" network for each …
Correction filter for single image super-resolution: Robustifying off-the-shelf deep super-resolvers
The single image super-resolution task is one of the most examined inverse problems in the
past decade. In the recent years, Deep Neural Networks (DNNs) have shown superior …
past decade. In the recent years, Deep Neural Networks (DNNs) have shown superior …
Adaptive sparse modeling in spectral & spatial domain for compressed image restoration
AS Arya, S Mukhopadhyay - Signal Processing, 2023 - Elsevier
Block discrete cosine transform (BDCT) is an indispensable component of modern image
and video coding standards, specifically for its decorrelation and superior energy …
and video coding standards, specifically for its decorrelation and superior energy …
Deep SURE for unsupervised remote sensing image fusion
HV Nguyen, MO Ulfarsson… - … on Geoscience and …, 2022 - ieeexplore.ieee.org
Image fusion is utilized in remote sensing (RS) due to the limitation of the imaging sensor
and the high cost of simultaneously acquiring high spatial and spectral resolution images …
and the high cost of simultaneously acquiring high spatial and spectral resolution images …
Deep unfolding of the DBFB algorithm with application to ROI CT imaging with limited angular density
M Savanier, E Chouzenoux… - IEEE Transactions on …, 2023 - ieeexplore.ieee.org
This article presents a new method for reconstructing regions of interest (ROI) from a limited
number of computed tomography (CT) measurements. Classical model-based iterative …
number of computed tomography (CT) measurements. Classical model-based iterative …
Augmented noise learning framework for enhancing medical image denoising
Deep learning attempts medical image denoising either by directly learning the noise
present or via first learning the image content. We observe that residual learning (RL) often …
present or via first learning the image content. We observe that residual learning (RL) often …
Miniature computational spectrometer with a plasmonic nanoparticles-in-cavity microfilter array
Optical spectrometers are essential tools for analysing light‒matter interactions, but
conventional spectrometers can be complicated and bulky. Recently, efforts have been …
conventional spectrometers can be complicated and bulky. Recently, efforts have been …
An unsupervised deep learning framework for medical image denoising
Medical image acquisition is often intervented by unwanted noise that corrupts the
information content. This paper introduces an unsupervised medical image denoising …
information content. This paper introduces an unsupervised medical image denoising …