Practical phase retrieval using double deep image priors
Phase retrieval (PR) concerns the recovery of complex phases from complex magnitudes.
We identify the connection between the difficulty level and the number and variety of …
We identify the connection between the difficulty level and the number and variety of …
Unsupervised deep learning for phase retrieval via teacher-student distillation
Phase retrieval (PR) is a challenging nonlinear inverse problem in scientific imaging that
involves reconstructing the phase of a signal from its intensity measurements. Recently …
involves reconstructing the phase of a signal from its intensity measurements. Recently …
Learning conditional generative models for phase retrieval
Reconstructing images from magnitude measurements is an important and difficult problem
arising in many research areas, such as X-ray crystallography, astronomical imaging and …
arising in many research areas, such as X-ray crystallography, astronomical imaging and …
Low-light phase retrieval with implicit generative priors
Phase retrieval (PR) is fundamentally important in scientific imaging and is crucial for
nanoscale techniques like coherent diffractive imaging (CDI). Low radiation dose imaging is …
nanoscale techniques like coherent diffractive imaging (CDI). Low radiation dose imaging is …
Learning-based lens wavefront aberration recovery
Wavefront aberration describes the deviation of a wavefront in an imaging system from a
desired perfect shape, such as a plane or a sphere, which may be caused by a variety of …
desired perfect shape, such as a plane or a sphere, which may be caused by a variety of …
Unlocking inverse problems using deep learning: Breaking symmetries in phase retrieval
In many physical systems, inputs related by intrinsic system symmetries generate the same
output. So when inverting such systems, an input is mapped to multiple symmetry-related …
output. So when inverting such systems, an input is mapped to multiple symmetry-related …
Compressive phase retrieval: Optimal sample complexity with deep generative priors
P Hand, O Leong, V Voroninski - Communications on Pure and …, 2024 - Wiley Online Library
Advances in compressive sensing (CS) provided reconstruction algorithms of sparse signals
from linear measurements with optimal sample complexity, but natural extensions of this …
from linear measurements with optimal sample complexity, but natural extensions of this …
LoDIP: Low light phase retrieval with deep image prior
Phase retrieval (PR) is a fundamental challenge in scientific imaging, enabling nanoscale
techniques like coherent diffractive imaging (CDI). Imaging at low radiation doses becomes …
techniques like coherent diffractive imaging (CDI). Imaging at low radiation doses becomes …
Optimizing intermediate representations of generative models for phase retrieval
Phase retrieval is the problem of reconstructing images from magnitude-only measurements.
In many real-world applications the problem is underdetermined. When training data is …
In many real-world applications the problem is underdetermined. When training data is …
Phase retrieval using single-instance deep generative prior
Phase Retrieval using Single-Instance Deep Generative Prior Page 1 Phase Retrieval using
Single-Instance Deep Generative Prior Kshitij Tayal*1 Raunak Manekar1 Zhong Zhuang 2 …
Single-Instance Deep Generative Prior Kshitij Tayal*1 Raunak Manekar1 Zhong Zhuang 2 …