Phase noise filtering and phase unwrapping method based on unscented Kalman filter

This paper presents a new phase unwrapping algorithm based on the unscented Kalman filter（UKF） for synthetic aperture radar（SAR） interferometry.This method is the result of combining an UKF with path-following strategy and an omni-directional local phase slope estimator.This technique performs simultaneously noise filtering and phase unwrapping along the high-quality region to the low-quality region,which is also able to avoid going directly through the noisy regions.In addition,phase slope is estimated directly from the sample frequency spectrum of the complex interferogram,by which the underestimation of phase slope is overcome.Simulation and real data processing results validate the effectiveness of the proposed method,and show a significant improvement with respect to the extended Kalman filtering（EKF） algorithm and some conventional phase unwrapping algorithms in some situations.

THE COHERENCE COEFFICIENT MAP AND RESIDUE GUIDED LEAST SQUARE PHASE UNWRAPPING ALGORITHM

The generation of high-resolution DEM from interferometric SAR has resulted in the need for accurate and efficient methods of 2-dimensional phase unwrapping. In this paper, we give a brief description of the mathematical base of phase unwrapping, and a detailed description of the unweighted and weighted least square phase unwrapping algorithm.Then our algorithm combining with the weighted least square phase unwrapping guided by the branch-cuts derived from Goldstein’ s algorithm and coherence coefficient map derived from the INSAR data is provided. In our experiment we write subroutines of the Goldstein’s branch-cut algorithm,unweighted and weighted least square phase unwrapping algorithm as well as our algorithm,and construct a small experiment system to resolve the phase unwrapping problem. Finally we test our algorithm on some INSAR data. The result shows that our approach can obtain unwrapped phase correctly and efficiently.

Enhanced multi-baseline unscented Kalman filtering phase unwrapping algorithm

This paper presents an enhanced multi-baseline phase unwrapping algorithm by combining an unscented Kalman filter with an enhanced joint phase gradient estimator based on the amended matrix pencil model, and an optimal path-following strategy based on phase quality estimate function. The enhanced joint phase gradient estimator can accurately and effectively extract the phase gradient information of wrapped pixels from noisy interferograms, which greatly increases the performances of the proposed method. The optimal path-following strategy ensures that the proposed algorithm simultaneously performs noise suppression and phase unwrapping along the pixels with high-reliance to the pixels with low-reliance. Accordingly, the proposed algorithm can be predicted to obtain better results, with respect to some other algorithms, as will be demonstrated by the results obtained from synthetic data.

A new method of weight choice in InSAR least squares unwrapping

The de-coherence phenomena such as Low-SNR radar signal, shadows and layover caused by topography, etc. , causing phase data discontinuity, makes the result of unwrapping phase inaccuracy or even completely wrong. Based on the analysis of influencing factors to weight choice, this thesis develops a new method to choose the weights based on the measure of the confidence in the frequency domain. Experiments show that it could overcome the defect of sub-estimate to the slope of least squares method very well, which has a better rationale, stability and performance.

A new method of weighted choice in InSAR Least Squares unwrapping

The decorelation phenomena such as Low-SNR radar signal, shadows and layover caused by topography etc, causes phase data discontinuous and makes the result of unwrapping phase inaccurate or completely wrong. Based on the analysis of influencing factors to the weight selection, this paper develops a new method to choose the weights based on the measurement of confidence in frequency domain. Results show that it is more precise and robust than other methods, and can make up for the defect of sub-estimate to the slope of least squares method.

Multi-baseline extended particle filtering phase unwrapping algorithm based on amended matrix pencil model and quantized path-following strategy

This paper proposes a new multi-baseline extended particle filtering phase unwrapping algorithm which combines an extended particle filter with an amended matrix pencil model and a quantized path-following strategy. The contributions to multibaseline synthetic aperture radar(SAR) interferometry are as follows: a new recursive multi-baseline phase unwrapping model based on an extended particle filter is built, and the amended matrix pencil model is used to acquire phase gradient information with a higher precision and lower computational cost, and the quantized path-following strategy is introduced to guide the proposed phase unwrapping procedure to efficiently unwrap wrapped phase image along the paths routed by a phase derivative variance map.

Comparison of phase unwrapping algorithms for living cell's interference pattern

A multiphase microscopic interference system is designed to measure the height of cell which is important to the research of collective cell migration in physiology and medicine. This sys- tem can quantitatively measure cell height across a living monolayer without knowing the refractive index of cells. For the interference pattern, because the phases are all wrapped between - π to π, it is necessary to get the real phase through phase unwrapping,a method to restore the wrapped phase data of the object by using numerical calculations. Three representative algorithms are selected to unwrap the interference pattern of ceils： branch-cut method, quality-guided method and network method. Although each of them can restore the phase, their performances are obviously different. We compare these methods and find that branch-cut method needs the smallest execution time and can obtain good unwrapped patterns when noises are not serious.

Investigation on Three Types of Residues in Phase-unwrapping

In phase unwrapping, how to control the residues and cut lines is critical to unwrap ill-state wrapped phase map successfully. Some new concepts in phase unwrapping field are proposed. Firstly, the residues in three types, i.e., singular residues, normal residues and virtual residues, are classified, which leads to some rational unwrapping paths. Secondly, some differences between cut-line and curved line are analyzed. And finally, experiment is carried out to compare our new enhanced Goldstein algorithm with the original Goldstein algorithm.

InSAR Kalman Filter Phase Unwrapping Algorithm Based on SRTM DEM

PU （phase unwrapping） is the key step and important problem in DEM （digital elevation model） extraction and the measurement of surface deformation of InSAR （Interferometric synthetic aperture radar）. The CKFPUA （conventional Kalman filter phase unwrapping algorithm） can obtain reliable results in the flat terrain areas, but it caused error transmission not making the accurate inversion of surface deformation information in the steep terrain. Considering this situation, so it needs to introduce topographic information for guiding phase unwrapping. Here the 90 m resolution DEM data have been used and it is obtained by SRTM （shuttle radar topography mission） measured jointly by NASA （National Aeronautics and Space Administration） and NIMA （National Imaging Mapping Agency） of U.S. Department of Defense. This paper presents a SD-KFPUA （Kalman filter phase unwrapping algorithm） based on SRTM DEM. With SRTM DEM directing InSAR image to implement phase unwrapping, the speed and accuracy are improved. By analyzing with the conventional Kalman filter phase unwrapping algorithms, it is shown that the proposed method can achieve good results in particular to improve unwrapping accuracy in the low coherence region.

Phase Unwrapping in Magnetic Resonance Elastography

Phase Unwrapping (PU) is an ill-posed problem in Magnetic Resonance Elastography (MRE). The phase information is not usable until the phases are retrieved by using PU algorithms. In this present study, we attempt to determine the ideal PU method for MRE using both phantom and volunteer psoas major (PM) muscle images. All the MRE experiments were carried out in Philips MRI (Achieva 3.0 T, Best, The Netherlands). A multi-echo gradient-echo MRE pulse sequence was employed and the four PU methods were considered based on their easy user platform. They are namely, Minimum Discontinuity (MD), Laplacian-Based Estimate (LBE), Region Growing (RG) and Dilate-Erode (DE) Propagate. Phantom images were successfully unwrapped by all four methods, whereas MD and LBE could only unwrap PM muscle images properly. RG and DE failed to unwrap the PM muscle images.

Segmentation-aided phase unwrapping for 3D reconstruction with airborne array-InSAR images

Unmanned aerial vehicle(UAV)array InSAR is a new type of single-flight 3D SAR imaging system with the advantages of high coherence and resolution.However,due to the low altitude of the platform,the elevation ambiguity of the system is smaller than the maximal terrain elevation.Since the spatial phase unwrapping is conducted based on the assumption of phase continuity,the inappropriate ambiguity will cause significant unwrapping errors.In this work,a 3D phase unwrapping algorithm assisted by image segmentation is proposed to address this issue.The Markov random field(MRF)is utilized for image segmentation.The optimal spatial phase unwrapping network is achieved based on the segmentation results.The asymptotic 3D phase unwrapping is adopted to get the refined 3D reconstruction.Results based on the real airborne array-InSAR data show that the proposed method effectively improves the elevation ambiguity.

Deep learning spatial phase unwrapping:a comparative review

Phase unwrapping is an indispensable step for many optical imaging and metrology techniques.The rapid development of deep learning has brought ideas to phase unwrapping.In the past four years,various phase dataset generation methods and deep-learning-involved spatial phase unwrapping methods have emerged quickly.However,these methods were proposed and analyzed individually,using different strategies,neural networks,and datasets,and applied to different scenarios.It is thus necessary to do a detailed comparison of these deep-learning-involved methods and the traditional methods in the same context.We first divide the phase dataset generation methods into random matrix enlargement,Gauss matrix superposition,and Zernike polynomials superposition,and then divide the deep-learning-involved phase unwrapping methods into deep-learning-performed regression,deep-learning-performed wrap count,and deep-learningassisted denoising.For the phase dataset generation methods,the richness of the datasets and the generalization capabilities of the trained networks are compared in detail.In addition,the deep-learning-involved methods are analyzed and compared with the traditional methods in ideal,noisy,discontinuous,and aliasing cases.Finally,we give suggestions on the best methods for different situations and propose the potential development direction for the dataset generation method,neural network structure,generalization ability enhancement,and neural network training strategy for the deep-learning-involved spatial phase unwrapping methods.

Phase unwrapping based on deep learning in light field fringe projection 3D measurement

Phase unwrapping is one of the key roles in fringe projection three-dimensional(3D)measurement technology.We propose a new method to achieve phase unwrapping in camera array light filed fringe projection 3D measurement based on deep learning.A multi-stream convolutional neural network(CNN)is proposed to learn the mapping relationship between camera array light filed wrapped phases and fringe orders of the expected central view,and is used to predict the fringe order to achieve the phase unwrapping.Experiments are performed on the light field fringe projection data generated by the simulated camera array fringe projection measurement system in Blender and by the experimental 3×3 camera array light field fringe projection system.The performance of the proposed network with light field wrapped phases using multiple directions as network input data is studied,and the advantages of phase unwrapping based on deep learning in light filed fringe projection are demonstrated.

A fringe jump counting method for the phase measurement in the HCN laser interferometer on EAST and its FPGA-based implementation

Electron density in fusion plasma is usually diagnosed using laser-aided interferometers. The phase difference signal obtained after phase demodulation is wrapped, which is also called a fringe jump. A method has been developed to unwrap the phase difference signal in real time using FPGA, specifically designed to handle fringe jumps in the hydrogen cyanide(HCN) laser interferometer on the EAST superconducting tokamak. This method is designed for a phase demodulator using the fast Fourier transform(FFT) method at the front end. The method is better adapted for hardware implementation compared to complex mathematical analysis algorithms, such as field programmable gate array(FPGA). It has been applied to process the phase measurement results of the HCN laser interferometer on EAST in real time. Electron density results show good confidence in the fringe jump unwrapping method. Further possible application in other laser interferometers, such as the POlarimeter-INTerferometer(POINT)system on EAST tokamak is also discussed.

Unwrapping and stereo rectification for omnidirectional images

Omnidirectional imaging sensors have been used in more and more applications when a very large field of view is required.In this paper,we investigate the unwrapping,epipolar geometry and stereo rectification issues for omnidirectional vision when the particular mirror model and the camera parameters are unknown in priori.First,the omnidirectional camera is calibrated under the Taylor model,and the parameters related to this model are obtained.In order to make the classical computer vision algorithms of conventional perspective cameras applicable,the ring omnidirectional image is unwrapped into two kinds of panoramas:cylinder and cuboid.Then the epipolar geometry of arbitrary camera configuration is analyzed and the essential matrix is deduced with its properties being indicated for ring images.After that,a simple stereo rectification method based on the essential matrix and the conformal mapping is proposed.Simulations and real data experimental results illustrate that our methods are effective for the omnidirectional camera under the constraint of a single view point.

Integrated phase unwrapping algorithm for the measurement of 3D shapes by Fourier transform profilometry

An integrated and reliable phase unwrapping algorithm is proposed based on residues and blocking-lines detection, closed contour extraction and quality map ordering for the measurement of 3D shapes by Fourier-transform profilometry (FTP). The proposed algorithm first detects the residues on the wrapped phase image, applies wavelet analysis to generate the blocking-lines that can just connect the residues of opposite polarity, then carries out the morphology operation to extract the closed contour of the shape, and finally uses the modulation intensity information and the Laplacian of Gaussian operation of the wrapped phase image as the quality map. The unwrapping process is completed from a region of high reliability to that of low reliability and the blocking-lines can prevent the phase error propagation effectively. Furthermore, by using the extracted closed contour to exclude the invalid areas from the phase unwrapping process, the algorithm becomes more efficient. The experiment shows the effec-tiveness of the new algorithm.

基于3dsmax的汽车涂装效果设计

文章对汽车涂装效果的计算机辅助设计进行了研究,论述了3dsmax Unwrap UVW贴图展开技术的原理和二维网格生成工具的特点。在该技术的支持下,可以先在3dsmax中依次完成车辆模型的建立和贴图展开,然后进行贴图方案的平面设计,并返回3dsmax渲染和展示。按照该方法测试了半挂车厢、轿车等案例,证明该方法可以用于汽车涂装效果设计。

Deep-learning-enabled dual-frequency composite fringe projection profilometry for single-shot absolute 3D shape measurement

Single-shot high-speed 3D imaging is important for reconstructions of dynamic objects.For fringe projection profilometry(FPP),however,it is still challenging to recover accurate 3D shapes of isolated objects by a single fringe image.In this paper,we demonstrate that the deep neural networks can be trained to directly recover the absolute phase from a unique fringe image that involves spatially multiplexed fringe patterns of different frequencies.The extracted phase is free from spectrum-aliasing problem which is hard to avoid for traditional spatial-multiplexing methods.Experiments on both static and dynamic scenes show that the proposed approach is robust to object motion and can obtain high-quality 3D reconstructions of isolated objects within a single fringe image.

DEFORMATION MEASUREMENT USING DUAL-FREQUENCY PROJECTION GRATING PHASE-SHIFT PROFILOMETRY

2π phase ambiguity problem is very important in phase measurement when a deformed object has a large out of plane displacement. The dual-frequency projection grating phaseshifting profilometry （PSP） can be used to solve such an issue. In the measurement, two properchosen frequency gratings are utilized to synthesize an equivalent wavelength grating which ensures the computed phase in a principal phase range. Thus, the error caused by the phase unwrapping process with the conventional phase reconstruct algorithm can be eliminated. Finally, experimental result of a specimen with large plastic deformation is given to prove that the proposed method is effective to handle the phase discontinuity.

AN ALGORITHM FOR INTERFEROMETRIC SAR DATA PROCESSING

In this paper, an algorithm of generating INSAR unwrapped phase image from SAR single-look complex images is presented. Besides the general processing technique, this article focuses on the methods of flat-earth phase removal, phase noise reduction and phase unwrapping. The availability is tested by the results of processing ERS-1/2 SAR images.