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DIP-UP: Deep Image Prior for Unwrapping Phase

https://doi.org/10.3390/info16070592
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What this work says

Outgoing claims: the papers this work cites, builds on, or argues with.

inferredcites →https://doi.org/10.1002/hbm.10062openalex-snapshot
Fast robust automated brain extraction
inferredcites →https://doi.org/10.1002/jmri.25045openalex-snapshot
Phase unwrapping in 4D MR flow with a 4D single-step laplacian algorithm
inferredcites →https://doi.org/10.1002/mrm.10354openalex-snapshot
Fast, automated, N‐dimensional phase‐unwrapping algorithm
inferredcites →https://doi.org/10.1002/mrm.21828openalex-snapshot
Calculation of susceptibility through multiple orientation sampling (COSMOS): A method for conditioning the inverse problem from measured magnetic field map to susceptibility source image in MRI
inferredcites →https://doi.org/10.1002/mrm.22074openalex-snapshot
Reliable two‐dimensional phase unwrapping method using region growing and local linear estimation
inferredcites →https://doi.org/10.1002/mrm.24765openalex-snapshot
Background field removal using spherical mean value filtering and Tikhonov regularization
inferredcites →https://doi.org/10.1002/mrm.25279openalex-snapshot
Robust phase unwrapping for MR temperature imaging using a magnitude‐sorted list, multi‐clustering algorithm
inferredcites →https://doi.org/10.1002/mrm.26989openalex-snapshot
Phase processing for quantitative susceptibility mapping of regions with large susceptibility and lack of signal
inferredcites →https://doi.org/10.1002/mrm.27011openalex-snapshot
General phase regularized reconstruction using phase cycling
inferredcites →https://doi.org/10.1002/mrm.27891openalex-snapshot
Analysis of different phase unwrapping methods to optimize quantitative susceptibility mapping in the abdomen
inferredcites →https://doi.org/10.1002/mrm.28927openalex-snapshot
The PHU‐NET: A robust phase unwrapping method for MRI based on deep learning
inferredcites →https://doi.org/10.1002/mrm.29265openalex-snapshot
Deep learning–based quantitative susceptibility mapping (QSM) in the presence of fat using synthetically generated multi‐echo phase training data
inferredcites →https://doi.org/10.1002/mrm.29465openalex-snapshot
A method for measuring B0 field inhomogeneity using quantitative double‐echo in steady‐state
inferredcites →https://doi.org/10.1002/nbm.1670openalex-snapshot
A novel background field removal method for MRI using projection onto dipole fields (PDF)
inferredcites →https://doi.org/10.1002/nbm.3601openalex-snapshot
An illustrated comparison of processing methods for MR phase imaging and QSM: combining array coil signals and phase unwrapping
inferredcites →https://doi.org/10.1002/nbm.4461openalex-snapshot
xQSM: quantitative susceptibility mapping with octave convolutional and noise‐regularized neural networks
inferredcites →https://doi.org/10.1002/nbm.4741openalex-snapshot
Proof of linear MRI phase imaging from an internal fieldmap
inferredcites →https://doi.org/10.1016/j.eswa.2023.121962openalex-snapshot
IC-GAN: An Improved Conditional Generative Adversarial Network for RGB-to-IR image translation with applications to forest fire monitoring
inferredcites →https://doi.org/10.1016/j.measurement.2022.111566openalex-snapshot
Two-dimensional phase unwrapping by a high-resolution deep learning network
inferredcites →https://doi.org/10.1016/j.neuroimage.2022.119410openalex-snapshot
Instant tissue field and magnetic susceptibility mapping from MR raw phase using Laplacian enabled deep neural networks
inferredcites →https://doi.org/10.1016/j.neuroimage.2024.120583openalex-snapshot
Quantitative susceptibility mapping through model-based deep image prior (MoDIP)
inferredcites →https://doi.org/10.1016/j.zemedi.2021.06.004openalex-snapshot
Deep grey matter quantitative susceptibility mapping from small spatial coverages using deep learning
inferredcites →https://doi.org/10.1016/j.zemedi.2022.08.001openalex-snapshot
BFRnet: A deep learning-based MR background field removal method for QSM of the brain containing significant pathological susceptibility sources
inferredcites →https://doi.org/10.1109/cvpr52733.2024.02379openalex-snapshot
Unsupervised Deep Unrolling Networks for Phase Unwrapping
inferredcites →https://doi.org/10.1109/isbi52829.2022.9761569openalex-snapshot
Structural-Mr Informed QSM Reconstruction Using Deep Image Prior
inferredcites →https://doi.org/10.1109/lsp.2018.2879184openalex-snapshot
PhaseNet: A Deep Convolutional Neural Network for Two-Dimensional Phase Unwrapping
inferredcites →https://doi.org/10.1109/tgrs.2022.3145342openalex-snapshot
PU-GAN: A One-Step 2-D InSAR Phase Unwrapping Based on Conditional Generative Adversarial Network
inferredcites →https://doi.org/10.1109/tip.2020.2977213openalex-snapshot
PhaseNet 2.0: Phase Unwrapping of Noisy Data Based on Deep Learning Approach
inferredcites →https://doi.org/10.1109/tip.2021.3099956openalex-snapshot
Robust Phase Unwrapping via Deep Image Prior for Quantitative Phase Imaging
inferredcites →https://doi.org/10.1109/tmi.2002.803106openalex-snapshot
Understanding phase maps in MRI: a new cutline phase unwrapping method
inferredcites →https://doi.org/10.1109/tmi.2018.2884093openalex-snapshot
SEGUE: A Speedy rEgion-Growing Algorithm for Unwrapping Estimated Phase
inferredcites →https://doi.org/10.1117/1.apn.1.1.014001openalex-snapshot
Deep learning spatial phase unwrapping: a comparative review
inferredcites →https://doi.org/10.1117/12.2537582openalex-snapshot
Temporal phase unwrapping using multi-scale deep neural networks
inferredcites →https://doi.org/10.1145/3422622openalex-snapshot
Generative adversarial networks
inferredcites →https://doi.org/10.1186/1471-2164-12-303openalex-snapshot
Close 3D proximity of evolutionary breakpoints argues for the notion of spatial synteny
inferredcites →https://doi.org/10.1364/ao.46.006623openalex-snapshot
Fast and robust three-dimensional best path phase unwrapping algorithm
inferredcites →https://doi.org/10.1364/ao.48.004582openalex-snapshot
Robust three-dimensional best-path phase-unwrapping algorithm that avoids singularity loops
inferredcites →https://doi.org/10.1364/oe.27.015100openalex-snapshot
One-step robust deep learning phase unwrapping
inferredcites →https://doi.org/10.1364/oe.27.023173openalex-snapshot
Rapid and robust two-dimensional phase unwrapping via deep learning
inferredcites →https://doi.org/10.1364/ol.28.001194openalex-snapshot
Fast phase unwrapping algorithm for interferometric applications
inferredcites →https://doi.org/10.21037/qims-22-525openalex-snapshot
New 3D phase-unwrapping method based on voxel clustering and local polynomial modeling: application to quantitative susceptibility mapping

What points at this work

Incoming claims: where this work appears in other papers’ reference lists and arguments.

inferred← citeshttps://doi.org/10.5281/zenodo.22091288zenodo-depositor-declared
inferred← citeshttps://doi.org/10.5281/zenodo.22809175zenodo-depositor-declared
inferred← citeshttps://doi.org/10.3390/rs18050808openalex-snapshot
FPUNet: A Fourier-Enhanced U-Net for Robust 2-D Phase Unwrapping of Noisy InSAR Interferograms
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