A review of deep learning methods for compressed sensing image reconstruction and its medical applications
Compressed sensing (CS) and its medical applications are active areas of research. In this
paper, we review recent works using deep learning method to solve CS problem for images …
paper, we review recent works using deep learning method to solve CS problem for images …
Regularising inverse problems with generative machine learning models
Deep neural network approaches to inverse imaging problems have produced impressive
results in the last few years. In this survey paper, we consider the use of generative models …
results in the last few years. In this survey paper, we consider the use of generative models …
Plug-and-play methods for magnetic resonance imaging: Using denoisers for image recovery
Magnetic resonance imaging (MRI) is a noninvasive diagnostic tool that provides excellent
soft-tissue contrast without the use of ionizing radiation. Compared to other clinical imaging …
soft-tissue contrast without the use of ionizing radiation. Compared to other clinical imaging …
Compressed sensing with deep image prior and learned regularization
We propose a novel method for compressed sensing recovery using untrained deep
generative models. Our method is based on the recently proposed Deep Image Prior (DIP) …
generative models. Our method is based on the recently proposed Deep Image Prior (DIP) …
Flatnet: Towards photorealistic scene reconstruction from lensless measurements
Lensless imaging has emerged as a potential solution towards realizing ultra-miniature
cameras by eschewing the bulky lens in a traditional camera. Without a focusing lens, the …
cameras by eschewing the bulky lens in a traditional camera. Without a focusing lens, the …
Diffraction tomography with a deep image prior
KC Zhou, R Horstmeyer - Optics express, 2020 - opg.optica.org
We present a tomographic imaging technique, termed Deep Prior Diffraction Tomography
(DP-DT), to reconstruct the 3D refractive index (RI) of thick biological samples at high …
(DP-DT), to reconstruct the 3D refractive index (RI) of thick biological samples at high …
Compressive spectral image reconstruction using deep prior and low-rank tensor representation
J Bacca, Y Fonseca, H Arguello - Applied optics, 2021 - opg.optica.org
Compressive spectral imaging (CSI) has emerged as an alternative spectral image
acquisition technology, which reduces the number of measurements at the cost of requiring …
acquisition technology, which reduces the number of measurements at the cost of requiring …
Differentiable imaging: A new tool for computational optical imaging
The field of computational imaging has made significant advancements in recent years, yet it
still faces limitations due to the restrictions imposed by traditional computational techniques …
still faces limitations due to the restrictions imposed by traditional computational techniques …
Robust reconstruction with deep learning to handle model mismatch in lensless imaging
Mask-based lensless imaging is an emerging imaging modality, which replaces the lenses
with optical elements and makes use of computation to reconstruct images from the …
with optical elements and makes use of computation to reconstruct images from the …
OCMR (v1. 0)--open-access multi-coil k-space dataset for cardiovascular magnetic resonance imaging
C Chen, Y Liu, P Schniter, M Tong, K Zareba… - arXiv preprint arXiv …, 2020 - arxiv.org
Cardiovascular MRI (CMR) is a non-invasive imaging modality that provides excellent soft-
tissue contrast without the use of ionizing radiation. Physiological motions and limited speed …
tissue contrast without the use of ionizing radiation. Physiological motions and limited speed …