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 …
Variational deep image restoration
This paper presents a new variational inference framework for image restoration and a
convolutional neural network (CNN) structure that can solve the restoration problems …
convolutional neural network (CNN) structure that can solve the restoration problems …
Noise2Recon: Enabling SNR‐robust MRI reconstruction with semi‐supervised and self‐supervised learning
Purpose To develop a method for building MRI reconstruction neural networks robust to
changes in signal‐to‐noise ratio (SNR) and trainable with a limited number of fully sampled …
changes in signal‐to‐noise ratio (SNR) and trainable with a limited number of fully sampled …
Review of quanta image sensors for ultralow-light imaging
The quanta image sensor (QIS) is a photon-counting image sensor that has been
implemented using different electron devices, including impact ionization-gain devices, such …
implemented using different electron devices, including impact ionization-gain devices, such …
Truncated residual based plug-and-play ADMM algorithm for MRI reconstruction
Plug-and-play alternating direction method of multiplier (PnP-ADMM) can be used to solve
the magnetic resonance imaging (MRI) reconstruction problem, which allows plugging the …
the magnetic resonance imaging (MRI) reconstruction problem, which allows plugging the …
Residual RAKI: A hybrid linear and non-linear approach for scan-specific k-space deep learning
Parallel imaging is the most clinically used acceleration technique for magnetic resonance
imaging (MRI) in part due to its easy inclusion into routine acquisitions. In k-space based …
imaging (MRI) in part due to its easy inclusion into routine acquisitions. In k-space based …
Pivotal Auto-Encoder via Self-Normalizing ReLU
N Goldenstein, J Sulam… - IEEE Transactions on …, 2024 - ieeexplore.ieee.org
Sparse auto-encoders are useful for extracting low-dimensional representations from high-
dimensional data. However, their performance degrades sharply when the input noise at test …
dimensional data. However, their performance degrades sharply when the input noise at test …
CNN-based classification of degraded images with awareness of degradation levels
Image classification needs to consider the existence of image degradations in practice.
Although degraded images have various levels of degradation, the degradation levels are …
Although degraded images have various levels of degradation, the degradation levels are …
Deep Learning Based Compression with Classification Model on CMOS Image Sensors.
V Palani, T Thanarajan, A Krishnamurthy… - Traitement du …, 2023 - search.ebscohost.com
The complementary metal oxide semiconductor (CMOS) technique is widely used in modern
manufacturing processes for the high compatibility. A novel metaheuristic with deep learning …
manufacturing processes for the high compatibility. A novel metaheuristic with deep learning …
The Secrets of Non-Blind Poisson Deconvolution
A Gnanasambandam, Y Sanghvi… - IEEE Transactions on …, 2024 - ieeexplore.ieee.org
Non-blind image deconvolution has been studied for several decades but most of the
existing work focuses on blur instead of noise. In photon-limited conditions, however, the …
existing work focuses on blur instead of noise. In photon-limited conditions, however, the …