Subscribe to the gold package and get unlimited access to Shamra Academy
Register a new userDynamic 3-D measurement based on fringe-to-fringe transformation using deep learning
73
0
0.0
(
0
)
Added by
Haotian Yu
Publication date
2019
fields
Electronic Engineering
and research's language is
English
Ask ChatGPT about the research
No Arabic abstract
Fringe projection profilometry (FPP) has become increasingly important in dynamic 3-D shape measurement. In FPP, it is necessary to retrieve the phase of the measured object before shape profiling. However, traditional phase retrieval techniques often require a large number of fringes, which may generate motion-induced error for dynamic objects. In this paper, a novel phase retrieval technique based on deep learning is proposed, which uses an end-to-end deep convolution neural network to transform a single or two fringes into the phase retrieval required fringes. When the objects surface is located in a restricted depth, the presented network only requires a single fringe as the input, which otherwise requires two fringes in an unrestricted depth. The proposed phase retrieval technique is first theoretically analyzed, and then numerically and experimentally verified on its applicability for dynamic 3-D measurement.
rate research
Read More
In many optical metrology techniques, fringe pattern analysis is the central algorithm for recovering the underlying phase distribution from the recorded fringe patterns. Despite extensive research efforts for decades, how to extract the desired phase information, with the highest possible accuracy, from the minimum number of fringe patterns remains one of the most challenging open problems. Inspired by recent successes of deep learning techniques for computer vision and other applications, here, we demonstrate for the first time, to our knowledge, that the deep neural networks can be trained to perform fringe analysis, which substantially enhances the accuracy of phase demodulation from a single fringe pattern. The effectiveness of the proposed method is experimentally verified using carrier fringe patterns under the scenario of fringe projection profilometry. Experimental results demonstrate its superior performance in terms of high accuracy and edge-preserving over two representative single-frame techniques: Fourier transform profilometry and Windowed Fourier profilometry.
Multiple works have applied deep learning to fringe projection profilometry (FPP) in recent years. However, to obtain a large amount of data from actual systems for training is still a tricky problem, and moreover, the network design and optimization still worth exploring. In this paper, we introduce computer graphics to build virtual FPP systems in order to generate the desired datasets conveniently and simply. The way of constructing a virtual FPP system is described in detail firstly, and then some key factors to set the virtual FPP system much close to the reality are analyzed. With the aim of accurately estimating the depth image from only one fringe image, we also design a new loss function to enhance the quality of the overall and detailed information restored. And two representative networks, U-Net and pix2pix, are compared in multiple aspects. The real experiments prove the good accuracy and generalization of the network trained by the data from our virtual systems and the designed loss, implying the potential of our method for applications.
We present the evaluation of a closed form formula for the calculation of the original step between two randomly shifted fringe patterns. Our proposal extends the Gram--Schmidt orthonormalization algorithm for fringe pattern. Experimentally, the phase shift is introduced by a electro--mechanical devices (such as piezoelectric or moving mounts).The estimation of the actual phase step allows us to improve the phase shifting device calibration. The evaluation consists of three cases that represent different pre-normalization processes: First, we evaluate the accuracy of the method in the orthonormalization process by estimating the test step using synthetic normalized fringe patterns with no background, constant amplitude and different noise levels. Second, we evaluate the formula with a variable amplitude function on the fringe patterns but with no background. Third, we evaluate non-normalized noisy fringe patterns including the comparison of pre-filtering processes such as the Gabor filter banks and the isotropic normalization process, in order to emphasize how they affect in the calculation of the phase step.
Atom interferometers offer excellent sensitivity to gravitational and inertial signals but have limited dynamic range. We introduce a scheme that improves on this trade-off by a factor of 50 using composite fringes, obtained from sets of measurements with slightly varying interrogation times. We analyze analytically the performance gain in this approach and the trade-offs it entails between sensitivity, dynamic range, and temporal bandwidth, and we experimentally validate the analysis over a wide range of parameters. By combining composite-fringe measurements with a particle-filter estimation protocol, we demonstrate continuous tracking of a rapidly varying signal over a span two orders of magnitude larger than the dynamic range of a traditional atom interferometer.
The fringe sensor unit (FSU) is the central element of the phase referenced imaging and micro-arcsecond astrometry (PRIMA) dual-feed facility for the Very Large Telescope interferometer (VLTI). It has been installed at the Paranal observatory in August 2008 and is undergoing commissioning and preparation for science operation. Commissioning observations began shortly after installation and first results include the demonstration of spatially encoded fringe sensing and the increase in VLTI limiting magnitude for fringe tracking. However, difficulties have been encountered because the FSU does not incorporate real-time photometric correction and its fringe encoding depends on polarisation. These factors affect the control signals, especially their linearity, and can disturb the tracking control loop. To account for this, additional calibration and characterisation efforts are required. We outline the instrument concept and give an overview of the commissioning results obtained so far. We describe the effects of photometric variations and beam-train polarisation on the instrument operation and propose possible solutions. Finally, we update on the current status in view of the start of astrometric science operation with PRIMA.
Log in to be able to interact and post comments
comments
Fetching comments
Sign in to be able to follow your search criteria
