Results 101 to 110 of about 222 (129)
Quantitative susceptibility mapping in the brain reflects spatial expression of genes involved in iron homeostasis and myelination. [PDF]
Hum Brain MappCohen Z, Lau L, Ahmed M, Jack CR, Liu C.
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Nucleosome spacing across cell types, diseases, and ages. [PDF]
Nucleic Acids ResBikova M, Clarkson CT, Teif VB.
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PU-M-Net for phase unwrapping with speckle reduction and structure protection in ESPI
Optics and Lasers in Engineering, 2022Abstract In this paper, we propose a deep nonlinear CNN model, named as PU-M-Net for phase unwrapping with speckle reduction and structure protection in ESPI. Our PU-M-Net consists of four pathways in “M” shape, and merges them by abundant skip connections.
Min Xu +4 more
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FCSN 3-D PU: Fully Connected Spatiotemporal Network Based 3-D Phase Unwrapping
IEEE Geoscience and Remote Sensing Letters, 2023Phase unwrapping (PU) based on spatial networks is a key procedure in time series synthetic aperture radar interferometry (TS-InSAR). Although the state-of-the-art techniques have shown good success in common cases, their performance remained uncertain in some challenging cases where the reliability of spatial networks is difficult, e.g., reservoir ...
Zhuang Gao +3 more
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PU-GAN: A One-Step 2-D InSAR Phase Unwrapping Based on Conditional Generative Adversarial Network
IEEE Transactions on Geoscience and Remote Sensing, 2022Two-dimensional phase unwrapping (PU) is a classical ill-posed problem in synthetic aperture radar interferometry (InSAR). The traditional algorithmic model-based 2-D PU methods are limited by the Itoh condition, which is from the PU researchers' experience and has critical challenges under strong phase noises or violent phase changes.
Lifan Zhou +3 more
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ViT-PU-Net: Volumetric Phase Unwrapping for MR images based on Vision Transformer
ISMRM Annual Meeting, 2023MR phase information has been used in many MRI applications, recently, so it is very important to estimate the correct MR phase. Since the MR phase is encoded in complex exponential, the actual phase information obtained is in the form of a wrapped signal.
Soohyun Jeon +2 more
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MoDL-PU: Model-Based Deep Learning for InSAR Phase Unwrapping
IEEE Transactions on Geoscience and Remote SensingLifan Zhou, Hanwen Yu
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SLBL-PU: Shadow-Based Layer-By-Layer Phase Unwrapping for Efficient 3D Measurement
IEEE Transactions on Computational ImagingRuiming Yu +5 more
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