This paper describes phase maps. A review of the phase unwrapping problem is given. Different structures, in particular fringelines, cutlines, and poles, contained within a phase map are described and their origin and behavior investigated. The problem of phase unwrapping can then be addressed with a better understanding of the source of poles or inconsistencies. This understanding, along with some assumptions about what is being encoded in the phase of a magnetic resonance image, are used to derive a new method for phase unwrapping which relies only on the phase map. The method detects cutlines and distinguishes between noise-induced poles and signal undersampling poles based on the length of the fringelines. The method was shown to be robust to noise and successful in unwrapping challenging clinical cases.
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http://dx.doi.org/10.1109/TMI.2002.803106 | DOI Listing |
We present a non-interferometric technique for quantitative phase imaging (QPI) that is cost-effective, easily integrated into standard microscopes, and capable of wide-field imaging with noncoherent light. Our method measures the phase gradient through optical differentiation using spatially variable amplitude filters, accommodating a range of transmission functions, including commercially available variable neutral-density filters. This flexibility is made possible by a general relationship we derive.
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December 2024
College of Civil Engineering, Xiangtan University, Xiangtan 411105, China.
Bridge expansion joints are critical components that accommodate the movement of a bridge caused by temperature fluctuations, concrete shrinkage, and vehicular loads. Analyzing the spatiotemporal deformation of these expansion joints is essential for monitoring bridge safety. This study investigates the deformation characteristics of Hongtang Bridge in Fuzhou, China, using synthetic aperture radar interferometry (InSAR).
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December 2024
Smart Computational Imaging Laboratory (SCILab), School of Electronic and Optical Engineering, Nanjing University of Science and Technology, Nanjing 210094, China.
Lens-free on-chip microscopy (LFOCM) is a powerful computational imaging technology that combines high-throughput capabilities with cost efficiency. However, in LFOCM, the phase recovered by iterative phase retrieval techniques is generally wrapped into the range of -π to π, necessitating phase unwrapping to recover absolute phase distributions. Moreover, this unwrapping process is prone to errors, particularly in areas with large phase gradients or low spatial sampling, due to the absence of reliable initial guesses.
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December 2024
Department of Electronic & Computer Engineer, University of Limerick, V94 T9PX Limerick, Ireland.
Current deep learning-based phase unwrapping techniques for iToF Lidar sensors focus mainly on static indoor scenarios, ignoring motion blur in dynamic outdoor scenarios. Our paper proposes a two-stage semi-supervised method to unwrap ambiguous depth maps affected by motion blur in dynamic outdoor scenes. The method trains on static datasets to learn unwrapped depth map prediction and then adapts to dynamic datasets using continuous learning methods.
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October 2024
School of Electrical Engineering, Yancheng Institute of Technology, Yancheng, Jiangsu, China.
3D printing is an indispensable technology in modern life and is widely used in aerospace, exoskeleton, and architecture. The increasing accuracy requirements of 3D printed objects in these fields require high-precision measurement methods to obtain accurate data. Based on the precision measurement requirements, in this study, a fast multifrequency phase unwrapping method based on 3D printing object appearance acquisition is proposed.
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