The influence of inhomogeneous gain on high-frequency phase of chemical laser beam

2013 ◽  
Author(s):  
Fu-xing Fu ◽  
Bin Zhang
Keyword(s):  
Laser Beam ◽  
High Frequency ◽  
Chemical Laser ◽  
Frequency Phase

Recovery of High Frequency Phase of Laser Beam with Wavefront Distortion

2011 ◽  
Vol 38 (4) ◽  
pp. 0402009 ◽  
Author(s):  
付福兴 Fu Fuxing ◽  
张彬 Zhang Bin
Keyword(s):  
Laser Beam ◽  
High Frequency ◽  
Wavefront Distortion ◽  
Frequency Phase

Analysis on recovery effect of high frequency phase of laser beam with wavefront distortion

2013 ◽  
Vol 25 (10) ◽  
pp. 2505-2510
Author(s):  
贺也洹 He Yehuan ◽  
韩开 Han Kai ◽  
张彬 Zhang Bin
Keyword(s):  
Laser Beam ◽  
High Frequency ◽  
Recovery Effect ◽  
Wavefront Distortion ◽  
Frequency Phase

The Influence of High-Frequency Phase of Laser Wavefront on the Distribution of Focal Spot

2014 ◽  
Vol 644-650 ◽  
pp. 1433-1437
Author(s):  
Fu Xing Fu
Keyword(s):  
Laser Beam ◽  
Surface Properties ◽  
High Frequency ◽  
Focal Spot ◽  
Laser Intensity ◽  
Side Lobe ◽  
Laser Beams ◽  
Damage Effect ◽  
Frequency Phase ◽  
Peak Value

When laser beam is effected on the material, the surface properties of material can be damaged, and the damage effect is different for the different laser beams. Nevertheless, the distribution of laser intensity is influenced by the proportion of high-frequency phase in wavefront. By researching the propagation character of wavefront phase, the relation of high-frequency phase and laser intensity is given in this paper. The results show that the focal spot increases gradually with the increase of the proportion of high-frequency phase, the peak value of intensity decreases obviously, and the number of side lobe increases observably.

High-frequency phase comparator

1965 ◽  
Vol 53 (11) ◽  
pp. 1784-1785
Author(s):  
E. Kantarizis
Keyword(s):  
High Frequency ◽  
Frequency Phase

scholarly journals Temporal phase unwrapping using deep learning

2019 ◽  
Vol 9 (1) ◽  
Author(s):  
Wei Yin ◽  
Qian Chen ◽  
Shijie Feng ◽  
Tianyang Tao ◽  
Lei Huang ◽  
...  
Keyword(s):  
Machine Learning ◽  
Deep Learning ◽  
High Frequency ◽  
Phase Unwrapping ◽  
Motion Artifacts ◽  
Error Sources ◽  
Actual Measurement ◽  
Temporal Phase ◽  
Fringe Patterns ◽  
Frequency Phase

AbstractThe multi-frequency temporal phase unwrapping (MF-TPU) method, as a classical phase unwrapping algorithm for fringe projection techniques, has the ability to eliminate the phase ambiguities even while measuring spatially isolated scenes or the objects with discontinuous surfaces. For the simplest and most efficient case in MF-TPU, two groups of phase-shifting fringe patterns with different frequencies are used: the high-frequency one is applied for 3D reconstruction of the tested object and the unit-frequency one is used to assist phase unwrapping for the wrapped phase with high frequency. The final measurement precision or sensitivity is determined by the number of fringes used within the high-frequency pattern, under the precondition that its absolute phase can be successfully recovered without any fringe order errors. However, due to the non-negligible noises and other error sources in actual measurement, the frequency of the high-frequency fringes is generally restricted to about 16, resulting in limited measurement accuracy. On the other hand, using additional intermediate sets of fringe patterns can unwrap the phase with higher frequency, but at the expense of a prolonged pattern sequence. With recent developments and advancements of machine learning for computer vision and computational imaging, it can be demonstrated in this work that deep learning techniques can automatically realize TPU through supervised learning, as called deep learning-based temporal phase unwrapping (DL-TPU), which can substantially improve the unwrapping reliability compared with MF-TPU even under different types of error sources, e.g., intensity noise, low fringe modulation, projector nonlinearity, and motion artifacts. Furthermore, as far as we know, our method was demonstrated experimentally that the high-frequency phase with 64 periods can be directly and reliably unwrapped from one unit-frequency phase using DL-TPU. These results highlight that challenging issues in optical metrology can be potentially overcome through machine learning, opening new avenues to design powerful and extremely accurate high-speed 3D imaging systems ubiquitous in nowadays science, industry, and multimedia.

High-frequency phase detector

1965 ◽  
Vol 1 (6) ◽  
pp. 171
Author(s):  
S. Tesic
Keyword(s):  
High Frequency ◽  
Phase Detector ◽  
Frequency Phase
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