On the use of deep learning for phase recovery
Phase recovery (PR) refers to calculating the phase of the light field from its intensity
measurements. As exemplified from quantitative phase imaging and coherent diffraction …
measurements. As exemplified from quantitative phase imaging and coherent diffraction …
Deep learning for digital holography: a review
Recent years have witnessed the unprecedented progress of deep learning applications in
digital holography (DH). Nevertheless, there remain huge potentials in how deep learning …
digital holography (DH). Nevertheless, there remain huge potentials in how deep learning …
Robust compressed sensing mri with deep generative priors
Abstract The CSGM framework (Bora-Jalal-Price-Dimakis' 17) has shown that
deepgenerative priors can be powerful tools for solving inverse problems. However, to date …
deepgenerative priors can be powerful tools for solving inverse problems. However, to date …
Wire: Wavelet implicit neural representations
Implicit neural representations (INRs) have recently advanced numerous vision-related
areas. INR performance depends strongly on the choice of activation function employed in …
areas. INR performance depends strongly on the choice of activation function employed in …
Deep learning techniques for inverse problems in imaging
Recent work in machine learning shows that deep neural networks can be used to solve a
wide variety of inverse problems arising in computational imaging. We explore the central …
wide variety of inverse problems arising in computational imaging. We explore the central …
Massively parallel functional photoacoustic computed tomography of the human brain
Abstract Blood-oxygen-level-dependent (BOLD) functional magnetic resonance imaging of
the human brain requires bulky equipment for the generation of magnetic fields …
the human brain requires bulky equipment for the generation of magnetic fields …
You only look yourself: Unsupervised and untrained single image dehazing neural network
In this paper, we study two challenging and less-touched problems in single image
dehazing, namely, how to make deep learning achieve image dehazing without training on …
dehazing, namely, how to make deep learning achieve image dehazing without training on …
Self-supervised neural networks for spectral snapshot compressive imaging
We consider using untrained neural networks to solve the reconstruction problem of
snapshot compressive imaging (SCI), which uses a two-dimensional (2D) detector to …
snapshot compressive imaging (SCI), which uses a two-dimensional (2D) detector to …
PET image denoising using unsupervised deep learning
Purpose Image quality of positron emission tomography (PET) is limited by various physical
degradation factors. Our study aims to perform PET image denoising by utilizing prior …
degradation factors. Our study aims to perform PET image denoising by utilizing prior …
Zero-shot noise2noise: Efficient image denoising without any data
Recently, self-supervised neural networks have shown excellent image denoising
performance. However, current dataset free methods are either computationally expensive …
performance. However, current dataset free methods are either computationally expensive …