Phase Correlation Method Research Articles

A Phase Correction Model for Fourier Transform Spectroscopy

In Fourier transform spectroscopy (FTS), the conventional Mertz method is commonly used to correct phase errors of recovered spectra, but it performs poorly in correcting nonlinear phase errors. This paper proposes a phase correlation method–all-pass filter (PCM-APF) model to correct phase errors. In this model, the proposed improved phase correlation method can correct linear phase errors, and all-pass filters are applied to correct the residual nonlinear phase errors. The optimization algorithm for the digital all-pass filters employs an improved algorithm which combines the subtraction-average-based optimizer (SABO) and the golden sine algorithm (Gold-SA). The proposed PCM-APF model demonstrates high correction precision, and the optimization algorithm for the filters converges faster than traditional intelligent optimization algorithms.

Исследование характеристик алгоритма корреляционно-экстремальной навигации для летательного аппарата

Correlation-extremal navigation and guidance systems are widely used in controlling the moving objects. The paper considers the aircraft correlation-extreme navigation algorithm designed for the aviation complexes. This algorithm provides radio identification and guidance for long-range detection of the enemy objects, as well as guidance of the destruction and interception weaponry against it. Classification of methods of the correlation-extreme navigation is presented, and each of the methods is being considered separately. Using the example of an airfield identified from the air, the error between the current and the reference images is determined as the distance between two points in the original image pixel coordinate system. When moving, the object image could be distorted by geometric, luminance and noise transformations. Using the found image, error dependencies on alterations in brightness, rotation angle and noise level of the image fragment, as well as the error dependencies on the selected navigation element, are investigated. Results are presented in the form of graphs. It is shown that the phase correlation method is more resistant to brightness and noise distortions of the image fragment, and the gradient correlation method is more resistant to the geometric transformations. It was found that navigation of heterogeneous and diverse navigation elements (cities) is better than the monotonous and homogeneous ones (roads and forests).

Optimizing cloud motion estimation on the edge with phase correlation and optical flow

Abstract. Phase correlation (PC) is a well-known method for estimating cloud motion vectors (CMVs) from infrared and visible spectrum images. Commonly, phase shift is computed in the small blocks of the images using the fast Fourier transform. In this study, we investigate the performance and the stability of the blockwise PC method by changing the block size, the frame interval, and combinations of red, green, and blue (RGB) channels from the total sky imager (TSI) at the United States Atmospheric Radiation Measurement user facility's Southern Great Plains site. We find that shorter frame intervals, followed by larger block sizes, are responsible for stable estimates of the CMV, as suggested by the higher autocorrelations. The choice of RGB channels has a limited effect on the quality of CMVs, and the red and the grayscale images are marginally more reliable than the other combinations during rapidly evolving low-level clouds. The stability of CMVs was tested at different image resolutions with an implementation of the optimized algorithm on the Sage cyberinfrastructure test bed. We find that doubling the frame rate outperforms quadrupling the image resolution in achieving CMV stability. The correlations of CMVs with the wind data are significant in the range of 0.38–0.59 with a 95 % confidence interval, despite the uncertainties and limitations of both datasets. A comparison of the PC method with constructed data and the optical flow method suggests that the post-processing of the vector field has a significant effect on the quality of the CMV. The raindrop-contaminated images can be identified by the rotation of the TSI mirror in the motion field. The results of this study are critical to optimizing algorithms for edge-computing sensor systems.

Accurate and fast registration algorithm for multi-height lensless in-line on-chip holographic microscopy

Lensless in-line on-chip holographic microscope has cost-effective and compact designs that can offer sub-micron resolution over a large field-of-view. In multi-height lensless on-chip holographic microscopy, the in-line hologram series are recorded at different positions along the optical axis, thus, there inevitably exists several kinds of error sources including shifts, scales, and rotations. Therefore, digitally registering and aligning these in-line holograms is of great concern for iterative phase-retrieved recovery. Here, we proposed an accurate and fast registration method, which consists of three major steps. First, Fourier-transform-based phase correlation method is used to roughly correct the spatial translation through locating the correlation peak. Second, we proposed a morphology-based method to accurately search for the matching feature points to obtain the accurate scaling factors, where samples themselves are used as markers without any labeling. Third, the phase correlation method is used for precisely registration again. Experimental results verified the validity and effectiveness of the proposed registration algorithm. And by the comparative experiments with the existing popular registration algorithms, our proposed registration algorithm can register and align the multi-height in-line holograms with greater precision and speed.

A New Image Grating Sensor for Linear Displacement Measurement and Its Error Analysis.

To improve the accuracy of the current vision-based linear displacement measurement in a large range, a new type of linear displacement sensing system, namely, image grating, is proposed in this paper. The proposed system included a patterned glass plate attached to the moving object and an ultra-low distortion lens for high-accuracy image matching. A DFT local up-sampling phase correlation method was adopted to obtain the sub-pixel translation of the patterns onto the target plate. Multiple sets of stripe patterns with different designs were located on the glass plate to expand the measurement range, based on the principle of phase correlation. In order to improve the measurement accuracy, the main errors of the image grating system were analyzed, and the nonlinear error compensation was completed based on the dynamic calibration of the pixel equivalent. The measurement results, after the error compensation, showed that the total error of the proposed system was less than 2.5 μm in the range of 60 mm, and the repeatability was within 0.16 μm, as quantified by standard deviation.

Efficient deformation mechanisms enable invasive cancer cells to migrate faster in 3D collagen networks

Cancer cell migration is a widely studied topic but has been very often limited to two dimensional motion on various substrates. Indeed, less is known about cancer cell migration in 3D fibrous-extracellular matrix (ECM) including variations of the microenvironment. Here we used 3D time lapse imaging on a confocal microscope and a phase correlation method to follow fiber deformations, as well as cell morphology and live actin distribution during the migration of cancer cells. Different collagen concentrations together with three bladder cancer cell lines were used to investigate the role of the metastatic potential on 3D cell migration characteristics. We found that grade-3 cells (T24 and J82) are characterized by a great diversity of shapes in comparison with grade-2 cells (RT112). Moreover, grade-3 cells with the highest metastatic potential (J82) showed the highest values of migration speeds and diffusivities at low collagen concentration and the greatest sensitivity to collagen concentration. Our results also suggested that the small shape fluctuations of J82 cells are the signature of larger migration velocities. Moreover, the displacement fields generated by J82 cells showed significantly higher fiber displacements as compared to T24 and RT112 cells, regardless of collagen concentration. The analysis of cell movements enhanced the fact that bladder cancer cells were able to exhibit different phenotypes (mesenchymal, amoeboid). Furthermore, the analysis of spatio-temporal migration mechanisms showed that cancer cells are able to push or pull on collagen fibers, therefore producing efficient local collagen deformations in the vicinity of cells. Our results also revealed that dense actin regions are correlated with the largest displacement fields, and this correlation is enhanced for the most invasive J82 cancer cells. Therefore this work opens up new routes to understand cancer cell migration in soft biological networks.

Registration and Combined Adjustment for the Laser Altimetry Data and High-Resolution Optical Stereo Images of the GF-7 Satellite

The GF-7 satellite is China’s first civil sub-meter resolution stereo mapping satellite, aiming at 1:10,000-scale mapping. To achieve this goal, apart from the stereo optical cameras that reach sub-meter resolution, the GF-7 satellite is equipped with a laser altimetry system capable of obtaining three-dimensional laser points (LPs) with high elevation accuracy. However, the combination of laser altimetry data and optical stereo images has not been thoroughly studied. In this paper, we exploit the images recorded by the highly integrated laser footprint cameras and propose a hierarchical phase correlation method based on a geographic pyramid for the registration of laser altimetry data and high-resolution optical stereo images, which lays a solid foundation for the following combined adjustment. Experiments show that the proposed registration method can automatically locate the LPs on high-resolution stereo images and meet the requirements of bundle adjustment. A series of bundle adjustment experiments were carried out, showing that laser altimetry data can significantly enhance the vertical accuracy of optical image stereo mapping and that elevation accuracy can reach roughly 1.0 m (RSME) without ground control points. Therefore, this study could be a good guide for global high-precision DSM acquisition with the GF-7 satellite.

Jitter correction for EAST infrared videos using edge-based phase correlation method

EAST has currently equipped with four wide-angle infrared (IR)/visible integrated endoscope diagnostics for routinely monitoring the status of plasma facing components (PFCs) and providing temperature information to ensure safe operation of the device. However, due to the existence of various sources of vibration force during the plasma experiments, image jitter can be clearly observed from the recorded IR videos, which increases the errors of temperature extraction from region of interest (ROI) within IR images and hence reduces the accuracy of heat flux calculation. In order to solve this problem, a method based on edge detection and phase correlation has been recently developed for correcting jitter of the IR videos on EAST, which has the advantages of high robustness, none accumulated error and high efficiency. Using this method, jitter of the IR video is successfully detected and corrected. Comparison between divertor heat flux results calculated from IR videos with and without jitter correction proves that the application of the edge-based phase correlation method for jitter correction can effectively improve the accuracy of the heat flux calculation.

Real-time high sensibility vibration detection based on phase correlation of line speckle patterns

A vibration extraction system based on laser speckle with phase correlation method is presented. The speckle pattern sequences are recorded by a linear array CMOS, which allows the system to detect high frequency signals more than 20 kHz. The line speckle patterns are fast Fourier transformed and correlated between the adjacent rows in the frequency domain. The phase angles of correlation calculations are directly used as the displacements between the respective two rows in the object space. The recovered audio signals are evaluated by segmental SNR (SegSNR), normalized sub-band envelope correlation (NSEC), and log-likelihood ratio (LLR). Compared with the centroid and quadratic fitting method, the phase correlation method obtains advantages of high stability, high sensibility, and real-time.

Automatic Sub-Pixel Co-Registration of Remote Sensing Images Using Phase Correlation and Harris Detector

In this paper, we propose a new approach for sub-pixel co-registration based on Fourier phase correlation combined with the Harris detector. Due to the limitation of the standard phase correlation method to achieve only pixel-level accuracy, another approach is required to reach sub-pixel matching precision. We first applied the Harris corner detector to extract corners from both references and sensed images. Then, we identified their corresponding points using phase correlation between the image pairs. To achieve sub-pixel registration accuracy, two optimization algorithms were used. The effectiveness of the proposed method was tested with very high-resolution (VHR) remote sensing images, including Pleiades satellite images and aerial imagery. Compared with the speeded-up robust features (SURF)-based method, phase correlation with the Blackman window function produced 91% more matches with high reliability. Moreover, the results of the optimization analysis have revealed that Nelder–Mead algorithm performs better than the two-point step size gradient algorithm regarding localization accuracy and computation time. The proposed approach achieves better accuracy than 0.5 pixels and outperforms the speeded-up robust features (SURF)-based method. It can achieve sub-pixel accuracy in the presence of noise and produces large numbers of correct matching points.

Displacement Identification by Computer Vision for Condition Monitoring of Rail Vehicle Bearings.

Bearings of rail vehicles bear various dynamic forces. Any fault of the bearing seriously threatens running safety. For fault diagnosis, vibration and temperature measured from the bogie and acoustic signals measured from trackside are often used. However, installing additional sensing devices on the bogie increases manufacturing cost while trackside monitoring is susceptible to ambient noise. For other application, structural displacement based on computer vision is widely applied for deflection measurement and damage identification of bridges. This article proposes to monitor the health condition of the rail vehicle bearings by detecting the displacement of bolts on the end cap of the bearing box. This study is performed based on an experimental platform of bearing systems. The displacement is monitored by computer vision, which can image real-time displacement of the bolts. The health condition of bearings is reflected by the amplitude of the detected displacement by phase correlation method which is separately studied by simulation. To improve the calculation rate, the computer vision only locally focuses on three bolts rather than the whole image. The displacement amplitudes of the bearing system in the vertical direction are derived by comparing the correlations of the image’s gray-level co-occurrence matrix (GLCM). For verification, the measured displacement is checked against the measurement from laser displacement sensors, which shows that the displacement accuracy is 0.05 mm while improving calculation rate by 68%. This study also found that the displacement of the bearing system increases with the increase in rotational speed while decreasing with static load.

ITKMontage: A Software Module for Image Stitching

Stitching partially overlapping image tiles into a montage is a common requirement for materials microscopy. We developed ITKMontage, a new module for the open-source Insight Toolkit (ITK), capable of robustly and quickly generating extremely large, high-bit-depth montages within the memory constraints of standard workstations. The phase correlation method is at the core of our pairwise tile registration algorithm. Precise alignment of tiles acquired in typical raster patterns is enhanced with sub-pixel fitting and cropping to overlap. Fast Fourier transform (FFT)-based correlation is improved through a variety of padding methods and an added adjustable bias toward an expected translation. To arrange the tiles into the overall montage, we use global least squares minimization with outlier detection and removal. To blend tiles smoothly, each tile’s contribution is weighted by the distance from the tile’s edge. Results are demonstrated on several material science data sets and 3D images. Results compare favorably to ImageJ/Fiji plugin Image Stitching. The tool is integrated into the DREAM.3D software suite for multidimensional, multimodal microstructural data.

МЕТОД КОМПЕНСАЦІЇ ПРОСТОРОВОЇ НЕВИЗНАЧЕНОСТІ ПРИ ВИМІРЮВАННІ У НАНОМЕТРОВОМУ ДІАПАЗОНІ

The article analyzes the main destabilizing factors that cause drift when measuring with a scanning probe microscope (SPM) in the nanometer range. It is determined that thermal drift is the main cause of spatial uncertainty in the mechanical system of SPM, because due to thermal expansion and compression can be displaced SPM probe or sample without applying any driving voltage to the piezoelectric drive mechanism. It has been established that thermal drift can have a significant effect on the reliability of measurement results in the form of a digital image, as it significantly distorts the position of the nanoobject during sequential scanning. Methods have been developed to compensate for the spatial uncertainty of the SPM and its piezoelectric drive mechanism, namely: hysteresis, creep, and thermal drift. It has been shown that hysteresis can be reduced by scanning in one direction, and it is recommended to pause for a few minutes after each major scan movement to eliminate creep. It is proved that drift in the z-direction can be considered as an uncorrelated value to the xy plane and can be eliminated by converting the original digital images to the gradient. Experiments have shown that the use of a phase correlation method to measure the offset of two gradient images is more effective than the use of original images. In addition, in two consecutive gradient images, there are no topographic changes, and using the Fourier transform shear property, the phase correlation method can convert the shift of the spatial region to the phase difference in the frequency domain. Calculation methods are proposed that analyze the effect of thermal expansion of the sensor drive on the overall drift of the system. The results showed that although the extension length of the sensor drive is the largest among all major system components, this may not be the main cause of overall system drift.

Measuring Solar Differential Rotation with an Iterative Phase Correlation Method

Abstract A reliable inference of the differential rotation rate of the solar photosphere is essential for models of the solar interior. The work presented here is based on a novel iterative phase correlation technique, which relies on the measurement of the local shift, at the central meridian, between two images separated by a given time interval. Consequently, it does not require any specific reference features, such as sunspots or supergranules, nor extended observations spanning several months. The reliability of the method is demonstrated by applying it to high spatial and temporal resolution continuum images of the solar photosphere, at 6173 Å, acquired by the Solar Dynamics Observatory Helioseismic and Magnetic Imager over one complete Carrington rotation. The data selected covers the time period of 2020 January 1 to February 2. The method was applied to one day, and to the full time interval. The differential rotation rate derived using this feature-independent technique yields values that fall in the middle of the range of those published to date. Most importantly, the method is suited for the production of detailed rotation maps of the solar photosphere. It also enables the visual and quantitative identification of the north–south asymmetry in the solar differential rotation rate, when present.

Robust global registration of point clouds by closed-form solution in the frequency domain

Point cloud registration is invariably an essential and challenging task in the fields of photogrammetry and computer vision to align multiple point clouds to a united reference frame. In this paper, we propose a novel global registration method using a robust phase correlation method for registration of low-overlapping point clouds, which is less sensitive to noise and outliers than feature-based registration methods. The proposed point cloud registration is achieved by converting the estimation of rotation, scaling, and translation in the spatial domain to a problem of correlating low-frequency components in the frequency domain. Specifically, it consists of three core steps: transformation from the spatial domain to the frequency domain, decoupling of rotation, scaling, and translation, and adapted phase correlation for robust shift estimation. In the first step, unstructured and unordered 3D points are transformed from the spatial domain to the frequency domain via 3D Fourier transformation, following a voxelization and binarization process. In the second step, rotation, scaling, and translation are decoupled by sequential operations, including Fourier transform, resampling strategies, and Fourier-Mellin transform. In the third step, the estimation of transformation parameters is transformed into shift estimation tasks. The shift estimation task is solved by a robust phase correlation method, in which low-frequency components are matched by decomposing the normalized cross-power spectrum and linearly fitting the decomposed signals with a closed-form solution by a ℓ1-norm-based robust estimator. Experiments were conducted using three different datasets of urban and natural scenarios. Results demonstrate the efficiency of the proposed method, with the majority of rotation and translation errors reaching less than 0.2 degree and 0.5 m, respectively. Additionally, it is also validated by experiments that the proposed method is robust to noise and versatile to datasets with wide ranges of overlaps and various geometric characteristics.

Rigid Motion Artifact Reduction in CT Using the Phase Correlation Method

Background: In Computed Tomography (CT), it is often not possible for the subject to remain stationary during a scan. Unfortunately, a patient motion would result in degraded spatial resolution and image artifacts. It is desirable to improve reconstruction quality and reduce motion artifacts caused by patient motion. Methods: In this work, a method was proposed to eliminate the influence of the motion on image quality, based on the phase correlation method. Based on our previous work, projections were first taken by Radon transform and motion parameters were estimated by the phase-only correlation of projections in the Radon domain. In addition, an improved image reconstruction algorithm was performed to compensate for the motion effects. Results: Experimental results proved that the proposed method could not only obtain high precision and good real-time performance but also ensure a superior artifact reduction. Conclusion: Besides, the efficacy of the proposed method has been demonstrated in both simulated and human head experiments.

An experimental sky-image-derived cloud validation dataset for Sentinel-2 and Landsat 8 satellites over NASA GSFC

Availability of a reliable cloud mask for optical satellite imagery is a prerequisite, when generating high-quality high-level geoinformation products. Creation of a reference (ground truth) cloud mask for moderate spatial resolution sensors, such as Operational Land Imager (OLI) aboard Landsat 8 and Multispectral Instrument (MSI) aboard Sentinel-2A/B satellites, is a challenging and time-consuming task. Existing reference datasets were mainly produced through photointerpretation of satellite images by an analyst, which can introduce subjectivity in detecting clouds. Therefore, other methods for generating cloud reference data shall be explored and evaluated that can complement existing datasets. In this paper, we document generation and provide the description of a new reference cloud dataset, named GSFC-Cloud, which is based on the extensive use of ground-based images of the sky. The dataset is collected over the same area, covers various cloud conditions, and is available for six Landsat 8 and twenty-eight Sentinel-2 scenes spanning the period of September 2017 to November 2018. The dataset is available in the vector format, so cloud masks at various spatial resolutions can be validated. We also describe a system to automate the process of ground-based data collection using low-cost off-the-shelf parts with the long-term objective to replicate this set-up in multiple locations around the world. We use the proposed dataset to validate and improve the Land Surface Reflectance Code (LaSRC) for cloud detection in Sentinel-2 imagery. We show that adding a parallax feature to estimate a subpixel shift between red and green bands with a phase correlation method can reduce overdetection of clouds and improve performance of LaSRC.

Efficient subpixel image registration algorithm for high precision visual vibrometry

As one of the most reliable motion estimation algorithms that apply phase correlation methods, the single-step DFT (SSDFT) approach has superior characteristics, including the high accuracy and low complexity. However, this approach is limited by the accuracy of the initial estimation. Therefore, it is an enormous challenge to reduce the dimension of the searching area in the subsequent refinement step. As a result, this algorithm is inefficient with large upsampling factors. In order to overcome this problem, an improved two-step image registration algorithm is proposed in the present study. In the first step, the accuracy of the initial estimation is improved by using a motion amplified cross-correlation function. The improved initial estimation is then amended to ensure that retained error is eliminated. In the refinement step, the dimension of the searching area is effectively reduced in accordance with the improved initial estimation and upsampling factor. Obtained results show that for large upsampling factors, the modified SSDFT achieves the same subpixel accuracy as the original algorithm. Meanwhile, it is found that the modified scheme remarkably reduces the computational expense. Finally, conducted experiments on high-speed video sequences demonstrate that the proposed modifications significantly reduce the required time for high precision target tracking tasks.

Robust Fine Registration of Multisensor Remote Sensing Images Based on Enhanced Subpixel Phase Correlation.

Automatic fine registration of multisensor images plays an essential role in many remote sensing applications. However, it is always a challenging task due to significant radiometric and textural differences. In this paper, an enhanced subpixel phase correlation method is proposed, which embeds phase congruency-based structural representation, L1-norm-based rank-one matrix approximation with adaptive masking, and stable robust model fitting into the conventional calculation framework in the frequency domain. The aim is to improve the accuracy and robustness of subpixel translation estimation in practical cases. In addition, template matching using the enhanced subpixel phase correlation is integrated to realize reliable fine registration, which is able to extract a sufficient number of well-distributed and high-accuracy tie points and reduce the local misalignment for coarsely coregistered multisensor remote sensing images. Experiments undertaken with images from different satellites and sensors were carried out in two parts: tie point matching and fine registration. The results of qualitative analysis and quantitative comparison with the state-of-the-art area-based and feature-based matching methods demonstrate the effectiveness and reliability of the proposed method for multisensor matching and registration.

A robust optical flow motion estimation and correction method for IRT imaging in brain surgery

ABSTRACT In brain surgery, respiration motion, outliers, and occlusions create artefacts in Infrared Thermography (IRT) imaging. In this paper, we propose a robust method to handle multiple motion, outliers, and occlusions in IRT images which consists of two phases: preprocessing and image motion estimation. In the preprocessing phase, the Region of Interest (RoI) segmentation is employed to extract the brain cortex only. Thereafter, the Phase Correlation method is employed to compensate for large motion followed by occlusion masking based on an approach applying Cellular Nonlinear Networks (CNN). Next, intensity adjustment is made with respect to the reference image. Then, a Gaussian filter is applied. In the following phase, the image motion is estimated by employing Combined Local-Global (CLG) optical flow method. In order to find the best regularization coefficient for the spatial coherency term and the number of iterations for recursive optical flow estimation, we generated ground truth and evaluated the accuracy of the estimated motion vectors based on Average Angular Error (AAE) and Average Magnitude Error (AME). The efficiency improvement of the proposed method was tested on 1024 IRT images based on different comparisons. Thereby, the proposed method shows promising results for motion estimation and correction application in brain surgery.