Autofocus Method Research Articles

Improved phase curvature autofocus for stripmap synthetic aperture radar imaging

Based on the theory of phase curvature autofocus (PCA) on stripmap synthetic aperture radar (SAR), an improved algorithm for increasing the accuracy of phase error compensation is presented in this study. PCA method was proposed to extend the phase gradient autofocus method for SAR systems in stripmap mode. The main problems concerned with the traditional PCA algorithm are related to selecting candidates in the image for phase error estimation, windowing, estimation procedure, and range shift due to the phase error. In this study, the modification of traditional PCA algorithm has been performed in different steps including the following: improving range-compressed data, prominent points extraction, adaptive windowing, weighted maximum likelihood for phase error estimation, improving phase error result, range shift compensation, and determining the condition to end the iterations. Real data experiments demonstrate the success of the proposed autofocus method, which is applied to the stretched-based pulsed mode SAR data set in the absence of highly accurate inertial navigation units.

Measuring anisotropy of elastic wave velocity with ultrasound imaging and an autofocus method: application to cortical bone

This work investigates the feasibility of estimating the parameters of an exact transverse isotropy model in cortical bone. The model describes the anisotropy of the velocity of compressional and shear bulk elastic waves. We propose to achieve this with ultrasound imaging relying on the transmission of unfocused beams and with an autofocus method. The latter is based on the principle that the reconstructed ultrasound image shows optimal quality if the velocity model is correct. The autofocus approach is applied to a composite image of the interface between cortical bone and marrow. It is obtained by incoherent summation of four types of images exploiting four different ray paths in the cortical bone layer, three of them involving mode-converted shear waves. If the parameters of the model are correct, spatial co-localization of the interface appears in the four images. As a result, intensity and sharpness in the composite image are maximal.The five parameters of the model of transverse isotropy are successfully estimated in a tube made of a bone-mimicking material. The estimates are in good agreement with resonant ultrasound spectroscopy (RUS) measurements. The tube thickness is recovered with an error smaller than 0.3%. In vivo results at the forearm of a volunteer are promising, four parameters could be estimated and are in good agreement with ex vivo RUS measurements. Moreover x-ray peripheral computed tomography corroborates the thickness of the cortical bone layer in the ultrasound image. Weak-anisotropy and exact transverse isotropy models provide very close measurements of the thickness of the tube and the radius bone. Thus, we recommend using the model of weak transverse isotropy for real-time anatomical imaging because more computationally efficient. For material characterization however, the model of exact transverse isotropy is preferred because the elastic anisotropy of cortical bone is moderate, rather than weak.

Efficient Nonparametric ISAR Autofocus Algorithm Based on Contrast Maximization and Newton’s Method

Phase autofocus is a significant step in translational motion compensation for inverse synthetic aperture radar (ISAR) imaging. Among the existing autofocus methods, contrast maximization-based algorithms are superior in accuracy and robustness. However, the existing maximum contrast autofocus methods are parametric, which significantly affects their convergence speed and, in some cases, limits their applicability. In this paper, a novel non-parametric maximum contrast ISAR autofocus algorithm based on Newton’s method is proposed to inherit the high level of accuracy and robustness of contrast-based algorithms and, at the same time, achieve faster convergence. The simplified Newton’s method, the modified Newton’s method, and the secant method are used to obtain a higher computational efficiency. The proposed method is then compared to a well-established non-parametric method, namely the minimum entropy autofocus method. It will be shown that the proposed method can improve the computational efficiency by a factor from 5 to 10 times in typical scenarios. Both simulated and real data will be used to test the proposed algorithm, particularly in terms of computational effectiveness.

Estimation and Compensation of Turbulent Tropospheric Delay in High-Resolution SAR Image

As a typical propagation error, tropospheric delay has been fully considered during synthetic aperture radar (SAR) interferometry and differential interferometry, while the influence on the SAR imaging process is just concerned in advanced configurations, e.g., staring mode, high-resolution, geosynchronous/geostationary (GEO) SAR. In this article, a comprehensive model of turbulent tropospheric delay (TTD), involving atmospheric sciences and fluid mechanics, is introduced, and the influence of which on high-resolution SAR is analyzed. In order to compensate for the influence of TTD in advanced SAR missions, the autofocus method used in motion error compensation is extended, and a 2-D phase error estimation and compensation algorithm is proposed. Subsequently, the simulation experiments of both dot-matrix targets and TerraSAR-X data are performed to validate the effectiveness of the proposed algorithm. Conclusions and prospects are reached in the end.

An Improved Map-Drift Algorithm for Unmanned Aerial Vehicle SAR Imaging

Unmanned aerial vehicle (UAV) synthetic aperture radar (SAR) is usually sensitive to trajectory deviations that cause severe motion error in the recorded data. Because of the small size of the UAV, it is difficult to carry a high-accuracy inertial navigation system. Therefore, in order to obtain a precise SAR imagery, autofocus algorithms, such as phase gradient autofocus (PGA) method and map-drift (MD) algorithm, were proposed to compensate the motion error based on the received signal, but most of them worked on range-invariant motion error and abundant prominent scatterers. In this letter, an improved MD algorithm is proposed to compensate the range-variant motion error compared to the existed MD algorithm. In this context, in order to solve the outliers caused by homogeneous scenes or absent prominent scatterers, a random sample consensus (RANSAC) algorithm is employed to mitigate the influence resulting from the outliers, realizing robust performance for different cases. Finally, real SAR data are applied to demonstrate the effectiveness of the proposed method.

Range‐dependent semi‐parametric autofocusing for manoeuvring targets inverse synthetic aperture imagery

Phase adjustment with the autofocus process is essential to inverse synthetic aperture radar (ISAR) imagery of manoeuvring targets. Conventional parametric and non-parametric autofocus methods usually degrade in front of error model mismatch and noise interference. Especially, the target manoeuvre also induces higher order phases that have the spatial-variant characteristic. In this study, a range-dependent semi-parametric autofocus algorithm for ISAR imagery is proposed. The range-variant phase is modelled semi-parametrically with a discrete cosine transform kernel. Implemented by the maximum sharpness optimisation, the algorithm well adapts to complex phase forms and enhances the robustness to strong noise. Accelerated solver to the optimisation is also introduced. Simulation and real data experiments confirm the advantages of the range-dependent semi-parametric autofocus algorithm compared with conventional algorithms.

Application of digital image processing for autofocusing system construction

The article states the method of autofocusing of the optoelectronic image, based on digital image processing. Some systems of autofocusing which can be used in optoelectronic equipment of unmanned aerial vehicles are considered. The method developed by authors and the program algorithm implementing it are similar to the rules of functioning of an element of the human visual system which executes an estimation of a spectrum of spatial frequencies for the realization of focusing of the image of objects which are of interest or danger and are on different distance from the observer. The best focusing is defined on the greatest width of a spectrum of spatial frequencies of the object. Such estimation allows focusing the necessary object on the observable image even in the absence of binocular vision. The method is based on the numerical analysis of a two-dimensional amplitude spectrum of the image. Results of operation of the stated method which confirm its efficiency are presented.

Virtual Source Extended Range Points Migration Method for Auto-Focusing 3-D Terahertz Imaging

This letter presents an auto-focusing imaging algorithm for terahertz (THz) band imaging by extending the range point migration (RPM) method using a dielectric lens model. The traditional THz time-domain spectroscopy (THz-TDS) imaging system has an essential problem for depth dependence of the azimuth resolution, which becomes more crucial in subsurface imaging, because the depth of target is unknown in most cases. This letter thus introduces an RPM-based auto-focusing imaging method using an equivalent virtual source model to accurately represent both near- and far-range areas from the focal point. Experimental validation using THz-TDS demonstrated that the proposed algorithm accurately compensates image distortions by out-of-focus effect with much lower complexity than traditional radar-imaging approaches.

Simple autofocusing method by image processing using transmission images for large-scale two-photon lithography.

We propose a simple autofocusing technique that can be introduced into conventional two-photon lithography systems without additional devices. Autofocusing is achieved by image processing using transmission images of photopolymerized voxels. The signal-to-noise ratio of transmission images was improved by optimal low-pass filtering to detect voxels in them. The focal point was detected with an accuracy of about 250 nm from the difference images. Further, we demonstrated mass-fabrication of a 5 × 5 spiral square array with an area of 900 × 900 µm2 using this method. The method has potential application in constructing low-cost, compact and versatile two-photon lithography apparatus.

Phase Gradient Matrix Autofocus for ISAL Space-Time-Varied Phase Error Correction

A new autofocus method of the space-time-varied phase error (PE) correction is presented for inverse synthetic aperture ladar (ISAL). The PE is corrected by a two-dimension matrix that is estimated in the form of phase gradient both in frequency and time domains. The autofocus method we named as PGMA is capable of compensating non-parametrical motion errors varying from pulse to pulse and is robust for widespread scene contents where any isolated strong scatterer is not necessary. The effectiveness is verified by comparing the imaging results of extended targets between PGMA and the conventional phase gradient autofocus (PGA) both in simulations and experiments. When the motion of targets contains both translational and angular motion errors, PGMA produces well-focused images while PGA merely corrects translational motion error, leaving the formatted images smear by angular motion error, especially to the edges.

Synthetic aperture radar autofocus based on phaseless measurements

We introduce a new framework for compressed sensing (CS) in synthetic aperture radar (SAR) imaging in the case of model error. Conventional CS-based autofocus methods solve a joint optimization problem to achieve both model error parameter estimation and SAR image formation simultaneously. Owing to the possibly nonconvex feature of the joint optimization problem, however, these algorithms may get stuck in local optima having large phase errors and thus fail to reconstruct the image. In contrast, we use phaseless measurements and pose imaging as a convex optimization problem. To solve the convex problem, we use the alternating direction method of multipliers-based approach, which is computationally efficient and easy to implement. The results from simulations with both point targets and extended targets validate the effectiveness of the proposed method.

A scanning and image processing system with integrated design for automated micronucleus scoring

Purpose: Our aim was to design a compact and cost-effective optical microscopic system for automated non-fluorescent micronucleus (MN) scoring whose performance can reach the accuracy of visual scoring with the help of minimal user interaction and also gives an option for fully automatic scoring with an accuracy suitable for triage purposes.Materials and methods: The concept of Radometer MN-Series (RS-MN) microscopic system designed by Radosys was to develop hardware and software layers in parallel in order to optimize the performance in automated MN scoring. A MN assay slide is automatically scanned by the RS-MN then the binucleated cells and micronuclei are automatically identified. Processing 1000 cells takes 10–60 minutes by automatic scoring (scanning plus image processing) depending on sample quality and required accuracy. The manual revision of the cell gallery takes an extra 10 minutes per sample. Dose response curves are determined for manual, automatic and semi-automatic scoring methods.Results: The combination of object-sensitive autofocusing method and the multi-layer image acquisition is able to reduce the minimum resolvable dose by 14%. The MN yields obtained from the manual, semi-automatic and automatic scoring methods are well correlating (Pearson’s correlation coefficients are between 0.977 and 0.998). In order to compare the reliability of the results of visual and automatic scoring, an extended analysis on uncertainty contributors was performed. For a dose of 1 Gy, the estimated relative uncertainty from the Poisson characteristics of MN yield is 17–19% for the manual and 20–38% for the automated scoring. Other uncertainty factors (differences in donor radiosensitivity, scorer performance, and sample preparations) can contribute to this error fall within a similar range: 3–16%. Taking into account all of the possible uncertainties, the minimum resolvable dose for the manual (0.48 Gy) is the two-thirds of that of the automatic scoring (0.61 Gy).Conclusions: The results verify that the fully automatic mode of RS-MN is suitable for triage purposes. The performance of the user interacted semi-automatic mode is comparable with the reference manual scoring. Its performance reaches up to other non-fluorescent automatic systems and offers a compact and cost-effective alternative.

A Nonparametric Paired Echo Suppression Method for Helicopter-Borne SAR Imaging

The helicopter-borne synthetic aperture radar (SAR) system suffers high-frequency vibrations caused by the rotor rotation. This micro-vibration induces phase modulation, which gives rise to paired echoes and seriously degrades the quality of SAR images due to the unexpected ghost phenomenon. In this letter, a nonparametric paired echo suppression method for helicopter-borne SAR imaging is proposed. By constructing a modified vibration error model and introducing the autofocus method for the vibration phase error estimation, it can suppress the paired echoes directly, which avoids the complex vibration parameter estimation in the traditional methods. The effectiveness of the proposed method is demonstrated via simulations and real data processing.

Accurate and Rapid Auto-Focus Methods Based on Image Quality Assessment for Telescope Observation

Aiming at improving the speed and accuracy of auto-focus for telescope observation, algorithms for image estimation and auto-focus were investigated and are discussed in this article. Based on the image quality assessment, the auto-focusing process of the telescope system is realized by using the mountain-climb search method. Several evaluation functions were tested in different scenarios. It is demonstrated that the Tenengrad image estimation function (IEF) is suitable for an instant and accurate auto-focus process of the telescope. Furthermore, we implemented sampling and dynamic adaptive focusing window (ES-DAFW) methods with the Tenengrad IEF to enhance the sensitivity and accuracy of the auto-focus process. The experimental results showed that our ES-DATW method can provide more accurate results in less time for the auto-focus process compared to the conventional approaches, especially for a sparse image. These results promise significant applications to the auto-focusing of other telescopes with image quality assessment.

Autofocusing and image fusion for multi-focus plankton imaging by digital holographic microscopy.

Digital holographic microscopy is becoming increasingly useful for the analysis of marine plankton. In this study, we investigate autofocusing and image fusion in digital holographic microscopy. We propose an area metric autofocusing method and an improved wavelet-based image fusion method. In the area metric autofocusing method, a hologram image is initially segmented into several plankton regions for focus plane detection, and an area metric is then applied to these regions. In the improved wavelet-based image fusion method, a marked map is introduced for labeling each plankton region with the order of refocus plane images that accounts for the most pixels. The results indicate that the area metric autofocusing method applied to each plankton region provides a higher depth resolution accuracy than a number of general autofocusing methods, and the mean accuracy increases by approximately 33%. The improved wavelet-based image fusion method can fuse more than nine reconstructed plane images at a time and effectively eliminate fringes and speckle noise, and the fused image is much clearer than that of a general wavelet-based method, a sparse decomposition method, and a pulse-coupled neural networks method. This work has practical value for plankton imaging using digital holographic microscopy.

基于自适应对焦窗口的计算鬼成像目标深度估计方法

基于自适应对焦窗口的计算鬼成像目标深度估计方法

Depth estimation in computational ghost imaging system using auto-focusing method with adaptive focus window

Depth estimation in computational ghost imaging system using auto-focusing method with adaptive focus window

A Non-Linear Correction Method for Terahertz LFMCW Radar

The nonlinearity in terahertz (THz) linear frequency modulated continuous wave (LFMCW) radar usually blurs the range profile and decreases the signal to noise ratio, hampering applications where high range-resolution is particularly emphasized. A software correction method, which comprises of transmitted nonlinearity estimation and nonlinear phase compensation for the beat signal, is proposed in this paper to drastically reduce the nonlinearity in THz LFMCW radar. Besides the commonly considered nonlinearity caused by voltage-controlled oscillator (VCO), the nonlinearity from other broadband hardware devices have also been included in our modified correction model, which gives the advantage of preciser compensation. Moreover, utilizing the phase gradient autofocus (PGA) method to estimate the transmitted nonlinear term and the residual video phase (RVP) removal method to remove the range dependency of the received nonlinearity, our method can uniformly compensate the nonlinearity in the whole range profile. In addition, no presupposed parametric model for the nonlinearity waveform is needed, which further strengthens the effectiveness of the proposed method in practical use. Both the simulated data and the real tested data, acquired by a 190 GHz radar with 60 GHz bandwidth, has been used to demonstrate the validity and the effectiveness of the method.

Design of Wide Angle and Large Aperture Optical System with Inner Focus for Compact System Camera Applications

Conventionally, a bright, very wide-angle optical system is designed as a floating type optical system that moves two or more lens groups composed of multiple lens in order to focus accurately. These have been widely used as phase detection auto focus (AF) methods within conventional digital single-lens reflex (DSLR) cameras. However, a phase detection AF optical system cannot be used when recording motion pictures. In contrast, a compact system camera (CSC) performs AF by the contrast method, where a stepper motor is used as the driving source for moving the optical lens. Nonetheless, to ensure that the focusing lens is lighter, these stepper motors should not have high torque and AF must be possible by moving only one lens. Yet, when focusing is performed with only one lens, aberration change due to focusing lens movement is magnified. Therefore, a very wide-angle optical system comprised of a half-angle of view more than 40 degrees and F of 1/4 has not been developed. Here, a very wide-angle optical system was designed with high resolving power that enables high speed AF, even in contrast mode, by moving only one lens while minimizing aberration change.

Precision 3-D Motion Tracking for Binocular Microscopic Vision System

In this paper, a three-dimensional (3-D) motion tracking method is proposed for binocular microscopic vision system to precisely record the motion trajectories of millimeter size objects in the Cartesian space. Primarily two fundamental problems are solved. The first problem arises from the limited depth of field (DOF) of microscope. Considering the motion of the objects, the existing autofocusing methods requiring sequential images either in focus or defocus are not workable. Therefore, a one-shot prior autofocusing approach is desired, which needs to take the motion tendency of objects into account. Besides, the autofocusing process always lags behind the motion of objects, and there inevitably will be prediction deviation on the motion tendency of objects. This leads to the second problem to estimate the 3-D motion states from defocused images. In this paper, we first explain the defocusing process from the perspective of S-Transform, based on which the Bayesian inference inspired method to estimate the depth from defocus from one single image is derived thereafter. Afterwards, a motion states, including both the position and velocity, estimation approach is developed within the Kalman filter framework. The above two aspects mutually supply the necessary information for each other to be functional to accurately realize the DOF tracking and motion tracking of moving objects. Experiments were well-designed to validate the effectiveness of the proposed method, and experiments result showed a tracking precision of 3 μ m was achieved.