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Microscopic hyperspectral imaging technology has been widely used to acquire pathological information of tissue sections. Autofocus is one of the most important steps in microscopic hyperspectral imaging systems to capture large scale or even whole slide images of pathological slides with high quality and high speed. However, there are quite few autofocus algorithm put forward for the microscopic hyperspectral imaging system. Therefore, this article proposes a Laplace operator based autofocus algorithm for microscopic hyperspectral imaging system which takes the influence of wavelength changes into consideration. Through the proposed algorithm, the focal length for each wavelength can be adjusted automatically to ensure that each single band image can be autofocused precisely with adaptive image sharpness evaluation method. In addition, to increase the capture speed, the relationship of wavelength and focal length is derived and the focal offsets among different single band images are calculated for pre‐focusing. We have employed the proposed method on our own datasets and the experimental results show that it can capture large‐scale microscopic hyperspectral pathology images with high precise. [ABSTRACT FROM AUTHOR]

For many parasitic diseases, the microscopic examination of clinical samples such as urine and stool still serves as the diagnostic reference standard, primarily because microscopes are accessible and cost-effective. However, conventional microscopy is laborious, requires highly skilled personnel, and is highly subjective. Requirements for skilled operators, coupled with the cost and maintenance needs of the microscopes, which is hardly done in endemic countries, presents grossly limited access to the diagnosis of parasitic diseases in resource-limited settings. The urgent requirement for the management of tropical diseases such as schistosomiasis, which is now focused on elimination, has underscored the critical need for the creation of access to easy-to-use diagnosis for case detection, community mapping, and surveillance. In this paper, we present a low-cost automated digital microscope—the Schistoscope—which is capable of automatic focusing and scanning regions of interest in prepared microscope slides, and automatic detection of Schistosoma haematobium eggs in captured images. The device was developed using widely accessible distributed manufacturing methods and off-the-shelf components to enable local manufacturability and ease of maintenance. For proof of principle, we created a Schistosoma haematobium egg dataset of over 5000 images captured from spiked and clinical urine samples from field settings and demonstrated the automatic detection of Schistosoma haematobium eggs using a trained deep neural network model. The experiments and results presented in this paper collectively illustrate the robustness, stability, and optical performance of the device, making it suitable for use in the monitoring and evaluation of schistosomiasis control programs in endemic settings. [ABSTRACT FROM AUTHOR]

Spaceborne synthetic aperture radar (SAR) systems, operating at L-band or lower, are very susceptible to ionospheric scintillation. The scintillation phase error (SPE) can bring about serious azimuth decorrelation and resolution loss. Compared with the motion phase error, the SPE takes on a much stronger spatial-variation both in along-track and across-track orientations, and its anisotropic feature makes the compensation more intractable. In this article, the phase gradient autofocus (PGA) is used for the first time to compensate the spatial-varying and anisotropic SPE for spaceborne SAR images. It begins with achieving a reliable SPE estimate by applying PGA to a strong scatterer. According to the preknowledge of the phase screen altitude and the anisotropic elongation heading, the emphasis is then on estimating the SPEs for other pixels from the well-estimated SPE, based on the mutual correlation and overlapping of SPEs. The pixel-by-pixel strategy is adopted for the final compensation. The proposed methodology is very appropriate for SAR images with few corner reflectors, which is validated on a simulated L-band stripmap data and a PALSAR-2 spotlight real data. It shows more powerful utility than the classical PGA. The performance analysis is implemented with regard to two necessary factors, which further confirms the effectiveness of the proposed methodology. [ABSTRACT FROM AUTHOR]

Respiratory motion-corrected coronary MR angiography (CMRA) has shown promise for assessing coronary disease. By incorporating coronal 2D image navigators (iNAVs), respiratory motion can be corrected for in a beat-to-beat basis using translational correction in the foot-head (FH) and right-left (RL) directions and in a bin-to-bin basis using non-rigid motion correction addressing the remaining FH, RL and anterior-posterior (AP) motion. However, with this approach beat-to-beat AP motion is not corrected for. In this work we investigate the effect of remaining beat-to-beat AP motion and propose a virtual 3D iNAV that exploits autofocus motion correction to enable beat-to-beat AP and improved RL intra-bin motion correction. Free-breathing 3D whole-heart CMRA was acquired using a 3-fold undersampled variable-density Cartesian trajectory. Beat-to-beat 3D translational respiratory motion was estimated from the 2D iNAVs in FH and RL directions, and in AP direction with autofocus assuming a linear relationship between FH and AP movement of the heart. Furthermore, motion in RL was also refined using autofocus. This virtual 3D (v3D) iNAV was incorporated in a non-rigid motion correction (NRMC) framework. The proposed approach was tested in 12 cardiac patients, and visible vessel length and vessel sharpness for the right (RCA) and left (LAD) coronary arteries were compared against 2D iNAV-based NRMC. Average vessel sharpness and length in v3D iNAV NRMC was improved compared to 2D iNAV NRMC (vessel sharpness: RCA: 56 ± 1% vs 52 ± 11%, LAD: 49 ± 8% vs 49 ± 7%; visible vessel length: RCA: 5.98 ± 1.37 cm vs 5.81 ± 1.62 cm, LAD: 5.95 ± 1.85 cm vs 4.83 ± 1.56 cm), however these improvements were not statistically significant. The proposed virtual 3D iNAV NRMC reconstruction further improved NRMC CMRA image quality by reducing artefacts arising from residual AP motion, however the level of improvement was subject-dependent. [ABSTRACT FROM AUTHOR]

Copyright of Journal of Chongqing University of Posts & Telecommunications (Natural Science Edition) is the property of Chongqing University of Posts & Telecommunications and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

In the field of digital imaging systems, autofocus plays increasingly a vital role as a key technology. Autofocus poses a great challenge due to nosiy background and slow focusing speed. This paper presents a new focusing algorithm based on improved Laplacian operator and mountain-climb search algorithm. The clear image after focusing is more different in gray scale than the image without focusing, an image definition evaluation function combining local variance and Laplacian operator is proposed. Learning from the advantages of two-stage recognition in deep learning image recognition, an two-stage search algorithm based on mountain-climb search is designed to better fit the focusing curve near the extreme value of focusing evaluation function, improved mountain-climb search algorithm is divided into rough focusing and fine focusing. The method of rough focusing is used to determine a small focus area, and then fine focusing based on function approximation can greatly improve the efficiency of focus position.The experimental results indicate that this algorithm in this paper is superior to the traditional algorithm in time and accuracy, and the time of the autofocus is reduced by 76%. [ABSTRACT FROM AUTHOR]

An intrinsic challenge in the geophysical interpretation of low-frequency synthetic aperture radar (SAR) imagery of semitransparent media, such as ice sheets, is the position ambiguity of the scattering structures within the glacial volume. Commonly tackled by applying interferometric and tomographic techniques, their spaceborne implementation exhibits by orders higher complexity compared to missions relying on single SAR images, making them cost expensive or, in the context of planetary missions, even impossible due to limited navigation capability. Besides, even these sophisticated techniques are commonly biased due to inaccurate permittivity estimates, leading to geometric distortions up to several meters. We present a novel inversion procedure to estimate volume parameters of ice sheets, namely, the depth of the scattering layer within the glacial volume and the dielectric permittivity of the ice, based on single-image single-polarization SAR acquisitions. The information is inherent in the processed SAR data as phase errors on the azimuth signals resulting from uncompensated nonlinear propagation of the radar echoes through ice. We suggest a local map-drift autofocus approach to quantify and spatially resolve the phase errors and an inversion model to relate them to the penetration depth and permittivity. Testing the proposed technique using P-band SAR data acquired using DLR’s airborne sensor F-SAR during the ARCTIC15 campaign in Greenland shows promising results and good agreement with tomographic products of the analyzed test site. [ABSTRACT FROM AUTHOR]

Wide-Angle (WA) synthetic aperture radar (SAR) has shown remarkable performance in high-resolution mapping, and its mostly used imaging algorithm, fast factorized back projection (FFBP), is efficient, robust, and of low computational complexity. However, trajectory deviations and the system calibration error, introduced by low measurement accuracy, dramatically degrade FFBP's performance. This article proposes the modified factorized geometrical autofocus (MFGA) method for WA-SAR to address the above problems in FFBP. MFGA implements the phase gradient algorithm on defocus subimages at first. Then, a factorized geometrical error hypothesis between subimages is proposed. And the corresponding defocus factors are classified as three independent parts: image distortion, spectrum migration, and phase error. To deal with those problems, MFGA introduces image registration techniques and a maximum sharpness method to calibrate image distortion and phase error. Moreover, in MFGA, based on minimum entropy and least square method, a Doppler spectrum migration correction algorithm is proposed to correct spectrum migration. In the FFBP chain, MFGA is used on subimages refocus and fusion until obtaining a full-resolution image. In our experiments, we compared MFGA and other time-domain autofocus algorithms using simulated data and real data obtained by helicopter and airship with false trajectory and system calibration parameters. The results show that MFGA performs better in terms of the peak to side-lobe ratio, the azimuth resolution, the refocused images' entropy, and processing time consumption. The better performance demonstrates MFGA's advantages in addressing trajectory deviations and the system calibration error for WA-SAR.

Copyright of Journal of Chongqing University of Posts & Telecommunications (Natural Science Edition) is the property of Chongqing University of Posts & Telecommunications and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

The OpenFlexure Microscope is a 3D‐printed, low‐cost microscope capable of automated image acquisition through the use of a motorised translation stage and a Raspberry Pi imaging system. This automation has applications in research and healthcare, including in supporting the diagnosis of malaria in low‐resource settings. The plasmodium parasites that cause malaria require high magnification imaging, which has a shallow depth of field, necessitating the development of an accurate and precise autofocus procedure. We present methods of identifying the focal plane of the microscope, and procedures for reliably acquiring a stack of focused images on a system affected by backlash and drift. We also present and assess a method to verify the success of autofocus during the scan. The speed, reliability and precision of each method are evaluated, and the limitations discussed in terms of the end users' requirements. [ABSTRACT FROM AUTHOR]

Snow water equivalent (SWE) is an important surface parameter for understanding a number of Earth system processes. Synthetic aperture radar (SAR) has considerable potential for measuring SWE of dry-snow because SAR can penetrate through the snow to the ground surface and is both amplitude- and phase-sensitive to refraction from the snow. Previous work on refraction-based SWE measurement by SAR has utilized the repeat-pass InSAR phase signal to estimate changes in SWE that occur between SAR acquisitions. These are subject to temporal decorrelation effects and consider only the refraction that occurs along the SAR beam center rather than the entire synthetic aperture. This study examines the refractive effect of dry-snow along the synthetic aperture and its impact on SAR image formation including defocusing and phase bias of the system impulse response. Snow phase compensation during time domain processing to recover the snow-free impulse response function (IRF) is described and demonstrated. The feasibility of using the mapdrift and image sharpness autofocus methods to estimate SWE is examined, and the effect of key system parameters on the estimation performance is derived. Experimental validation of the method was conducted by acquiring L-band data with the Simon Fraser University (SFU) Airborne SAR System over a pair of corner reflectors installed on the Kluane icefield in northwestern Canada. Results from both simulations and the icefield experiment are presented and compared including an analysis of errors affecting the estimation. [ABSTRACT FROM AUTHOR]

In the existing time-domain autofocus algorithms, the azimuth deramping operation will change the azimuth-independent phase into the azimuth-dependent phase, which may greatly reduce the accuracy of autofocus processing in squint spotlight synthetic aperture radar (SAR). In contrast, the frequency-domain autofocus algorithms can avoid this problem because it does not involve the azimuth deramping operation. However, the existing frequency-domain autofocus algorithms are proposed based on the assumption of broadside mode, which cannot be directly applied to the squint mode. Therefore, this article extends the existing frequency-domain autofocus algorithm to the squint mode combined with the extended Omega-K (EOK) algorithm. Furthermore, a space division (SD) algorithm is embedded into the proposed algorithm as preprocessing, which can effectively compensate for the azimuth-dependent motion error. The simulation and real data are processed to verify the effectiveness of the algorithm. [ABSTRACT FROM AUTHOR]

Studying the three-dimensional structure of paper fibrous networks is an important step towards understanding the relationship between manufacturing conditions and the resulting microstructural and mechanical properties. Digital holography is a promising three-dimensional imaging technique enabling quantitative phase evaluation of micro and nano-fibres. One of the advantages of this high-resolution method is the ability to perform numerical refocusing at several depths from a single shot acquisition. In this work, the suitability of 22 focusing functions to accurately identify the positions of cellulose microfibers, using digital holography, was inspected. The best performing metrics were identified and the trade-off between metric accuracy and robustness to variable conditions and their computational complexity were discussed. [ABSTRACT FROM AUTHOR]

Currently, camera modules are used in a wide range of applications ranging from micropo-sitioning actuator systems used in smartphones to larger modules used in stationary cameras, CCTV, and others. Modern camera modules are required to perform positioning functions to improve the stability and quality of captured images. These functions mainly include the autofocus (AF) and optical image stabilization (OIS) functions. Traditionally, separate actuators and sensors are used for AF and OIS, each contributing towards energy consumption, module size, and manufacturing costs. This is particularly disadvantageous in the case of smartphones owing to their inherent space and battery charge limitations. This paper presents a novel Magnetic Shape Memory (MSM) actuator system that combines the AF and OIS functions, which are performed using two actuators; thus, this system is a simple, compact, and cost-effective solution to the drawbacks of conventional technology. Furthermore, the smart properties of MSM alloys reduce the energy consumption of these systems through the utilization of their inherent holding forces and the associated shape memory effect. The realization of AF and OIS functions is discussed and verified experimentally for the proposed actuator system using data obtained from a fabricated prototype.

Accurate three-dimensional positioning of particles is a critical task in microscopic particle research, with one of the main challenges being the measurement of particle depths. In this paper, we propose a method for detecting particle depths from their blurred images using the depth-from-defocus technique and a deep neural network-based object detection framework called you-only-look-once. Our method provides simultaneous lateral position information for the particles and has been tested and evaluated on various samples, including synthetic particles, polystyrene particles, blood cells, and plankton, even in a noise-filled environment. We achieved autofocus for target particles in different depths using generative adversarial networks, obtaining clear-focused images. Our algorithm can process a single multi-target image in 0.008 s, allowing real-time application. Our proposed method provides new opportunities for particle field research.

Focus accuracy affects the quality of the astronomical observations. Auto-focusing is necessary for imaging systems designed for astronomical observations. The automatic focus system searches for the best focus position by using a proposed search algorithm. The search algorithm uses the image's focus levels as its objective function in the search process. This paper aims to study the performance of several search algorithms to select a suitable one. The proper search algorithm will be used to develop an automatic focus system for Kottamia Astronomical Observatory (KAO). The optimal search algorithm is selected by applying several search algorithms into five sequences of star-clusters observations. Then, their performance is evaluated based on two criteria, which are accuracy and number of steps. The experimental results show that the Binary search is the optimal search algorithm. [ABSTRACT FROM AUTHOR]

Trypan blue dye exclusion‐based cell viability measurements are highly dependent upon image quality and consistency. In order to make measurements repeatable, one must be able to reliably capture images at a consistent focal plane, and with signal‐to‐noise ratio within appropriate limits to support proper execution of image analysis routines. Imaging chambers and imaging systems used for trypan blue analysis can be inconsistent or can drift over time, leading to a need to assure the acquisition of images prior to automated image analysis. Although cell‐based autofocus techniques can be applied, the heterogeneity and complexity of the cell samples can make it difficult to assure the effectiveness, repeatability and accuracy of the routine for each measurement. Instead of auto‐focusing on cells in our images, we add control beads to the images, and use them to repeatedly return to a reference focal plane. We use bead image features that have stable profiles across a wide range of focal values and exposure levels. We created a predictive model based on image quality features computed over reference datasets. Because the beads have little variation, we can determine the reference plane from bead image features computed over a single‐shot image and can reproducibly return to that reference plane with each sample. The achieved accuracy (over 95%) is within the limits of the actuator repeatability. We demonstrate that a small number of beads (less than 3 beads per image) is needed to achieve this accuracy. We have also developed an open‐source Graphical User Interface called Bead Benchmarking‐Focus And Intensity Tool (BB‐FAIT) to implement these methods for a semi‐automated cell viability analyser. [ABSTRACT FROM AUTHOR]

With the development of miniaturization technology, Synthetic Aperture Radar (SAR) can be equipped on small carriers such as small Unmanned Aerial Vehicles (UAVs). In order to lower the cost, the accuracy of navigation equipment carried by UAV SAR is usually limited, so it is challenging to meet the requirements of SAR imaging and locating accuracy. Therefore, accurately estimating SAR tracks becomes a crucial issue. So, for the motion error estimation model widely used in current literature, this paper derives the accuracy limits of the model for the first time. The derived Cramer–Rao Lower Bound (CRLB) specifies the factors affecting the estimation accuracy, which provides new insights into the estimation model. The in-depth analysis of how the factors affect CRLB can guide the setting of the parameters while using the estimation method. Moreover, based on the accuracy analysis model, this paper improves the WTLS-based autofocus method (WTA) by selecting the appropriate estimation kernel step. The proposed method can suppress noise more effectively and further ensure estimation accuracy compared to WTA. Airborne SAR data experiments in the high-resolution condition obtain trajectory estimation results of 0.02 m.

Bistatic synthetic aperture radar (BiSAR) has drawn increasing attention in recent studies benefiting from its ability for forward-looking imaging, its capability of receiver radio silence and its resistance to jamming. However, the motion trajectory error compensation of BiSAR is a challenging task due to multiple error sources and complex effects. In this paper, an estimation and compensation method for three-dimensional (3D) motion trajectory error of BiSAR is proposed. In this method, the Doppler error of multiple scattering points is estimated firstly by using the time–frequency analysis method. Next, a local autofocus process is introduced to improve the Doppler error estimation accuracy. Then, the 3D trajectory error of BiSAR is estimated by solving a series of linear equations of the trajectory error and the Doppler error with the least squares method, and a well-focused BiSAR image is produced by using the corrected 3D trajectories. Finally, simulation and experiment results are presented to demonstrate the effectiveness of the proposed method.

An algorithm for a sharpness evaluation of microscopic images based on non-subsampled shearlet wave transform (NSST) and variance is proposed in the present study for the purpose of improving the noise immunity and accuracy of a microscope’s image autofocus. First, images are decomposed with the NSST algorithm; then, the decomposed sub-band images are subjected to variance to obtain the energy of the sub-band coefficients; and finally, the evaluation value is obtained from the ratio of the energy of the high- and low-frequency sub-band coefficients. The experimental results show that the proposed algorithm delivers better noise immunity performance than other methods reviewed by this study while maintaining high sensitivity.

Featured Application: The technique described herein is a method for linearizing the frequency modulation error of a Frequency-Modulated/Continuous Wave (FMCW) radar as part of range migration processing when forming SAR images that results in no additional computation cost. Linear FMCW radar suffers from impairments in range and range rate if there are errors in the modulation rate or phase discontinuities. Often, this is a result of a nonlinearity of the voltage-controlled oscillator that is in the source of the transmit and receive local oscillator. The nonlinearity can be corrected at the source by using a nonlinear control voltage or by processing the received beat frequency. Any signal processing using the later method leads to computation time and energy costs, which can be considerable in some applications. When the range migration algorithm using the Stolt Transform is used for Synthetic Aperture Radar (SAR) image processing, the autofocus linearization technique described here costs nothing in additional hardware or computation time. [ABSTRACT FROM AUTHOR]

Wearable Video See-Through (VST) devices for Augmented Reality (AR) and for obtaining a Magnified View are taking hold in the medical and surgical fields. However, these devices are not yet usable in daily clinical practice, due to focusing problems and a limited depth of field. This study investigates the use of liquid-lens optics to create an autofocus system for wearable VST visors. The autofocus system is based on a Time of Flight (TOF) distance sensor and an active autofocus control system. The integrated autofocus system in the wearable VST viewers showed good potential in terms of providing rapid focus at various distances and a magnified view. [ABSTRACT FROM AUTHOR]

This paper proposes a fast inverse synthetic aperture radar (ISAR) motion compensation (MOCOM) method. The various motion compensation methods using minimum entropy algorithm such as stage-by-stage approaching (SSA) algorithm were reported recently. However, the computational efficiency and the clearance of the ISAR image were hard to achieve simultaneously. In particular, as the number of the local minima in the entropy curve increases, the processing time takes longer to find the global minimum. In this paper, we propose a new MOCOM algorithm based on an improved SSA algorithm to reduce the processing time. The proposed algorithm uses the envelope value as the fitness function instead of the entropy value, so the number of the local minima in the entropy curve is reduced. Therefore, the entropy curve becomes simple and the required number of the phase error samples is reduced. In the simulation, the processing time is improved by 88.8% than the conventional SSA algorithm. Also, in the experiment, the processing time is improved by 87.9% ∼ 89.2%. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

This paper introduces an improved filtering operator based on the first-order derivative Gaussian filtering operator. As the defocus distance decreases, the edges of an image transition from being smooth to sharp. Using the operator proposed in this study, a parameter can be obtained to quantify the degree of edge sharpness, where a smaller value indicates sharper edges. By using the characteristic, we can judge whether the object is near the focal plane. Combining this information, we proposed two focus method. Both of them can avoid the problem of the hill climbing algorithm locating at local extremum points and one of them can locate the watershed between "coarse search" and "fine search" before the sharpness value crosses the peak value, so as to realize the transition from "coarse search" to "fine search" before crossing the peak value and improve focusing efficiency. This is different from previous passive focusing strategies. The final experiment shows that the proposed solutions in this paper is effective. However, the methods proposed in this study are only applicable to incoherent optical microscopy imaging systems and are not suitable for coherent light source microscopy imaging systems. Besides, the prerequisite for the successful implementation of the two proposed focusing strategies in this study is that the target exhibits sharp edges when it is within the focal plane. • A sharpness measurement parameter with a lower bound. • An autofocus strategy for microscope without repeated motion. • An autofocus strategy is more stable than and similar time-consuming to hill-climbed method. [ABSTRACT FROM AUTHOR]

PurposeTo implement a nonrigid autofocus motion correction technique to improve respiratory motion correction of free-breathing whole-heart coronary magnetic resonance angiography acquisitions using an image-navigated 3D cones sequence.Methods2D image navigators acquired every heartbeat are used to measure superior-inferior, anterior-posterior, and right-left translation of the heart during a free-breathing coronary magnetic resonance angiography scan using a 3D cones readout trajectory. Various tidal respiratory motion patterns are modeled by independently scaling the three measured displacement trajectories. These scaled motion trajectories are used for 3D translational compensation of the acquired data, and a bank of motion-compensated images is reconstructed. From this bank, a gradient entropy focusing metric is used to generate a nonrigid motion-corrected image on a pixel-by-pixel basis. The performance of the autofocus motion correction technique is compared with rigid-body translational correction and no correction in phantom, volunteer, and patient studies.ResultsNonrigid autofocus motion correction yields improved image quality compared to rigid-body-corrected images and uncorrected images. Quantitative vessel sharpness measurements indicate superiority of the proposed technique in 14 out of 15 coronary segments from three patient and two volunteer studies.ConclusionThe proposed technique corrects nonrigid motion artifacts in free-breathing 3D cones acquisitions, improving image quality compared to rigid-body motion correction.

Airborne synthetic aperture radar (SAR) systems often encounter the threats of interceptors or electronic countermeasures (ECM) and suffer from motion measurement errors. In order to design and analyze SAR systems while considering such threats and errors, an integrated raw data simulator is proposed for airborne spotlight electronic counter-countermeasure (ECCM) SAR. The raw data for reflected echo signals and jamming signals are generated in arbitrary waveform to achieve pulse diversity. The echo signals are simulated based on the scene model computed through the inverse polar reformatting of the reflectivity map. The reflectivity map is generated by applying a noise-like speckle to an arbitrary grayscale optical image. The received jamming signals are generated by the jamming model, and their powers are determined by the jamming equivalent sigma zero (JESZ), a newly proposed quantitative measure for designing ECCM SAR systems. The phase errors due to the inaccuracy of the navigation system are also considered in the design of the proposed simulator, as navigation sensor errors were added in the motion measurement process, with the results used for the motion compensation. The validity and usefulness of the proposed simulator is verified through the simulation of autofocus algorithms, SAR jamming, and SAR ECCM with pulse diversity. Various types of autofocus algorithms were performed through the proposed simulator and, as a result, the performance trends were identified to be similar to those of the real data from actual flight tests. The simulation results of the SAR jamming and SAR ECCM indicate that the proposed JESZ is well-defined measure for quantifying the power requirements of ECCM SAR and SAR jammers.

Enhancing quality image fusion was proposed using new algorithms in auto-focus image fusion. The first algorithm is based on determining the standard deviation to combine two images. The second algorithm concentrates on the contrast at edge points and correlation method as the criteria parameter for the resulted image quality. This algorithm considers three blocks with different sizes at the homogenous region and moves it 10 pixels within the same homogenous region. These blocks examine the statistical properties of the block and decide automatically the next step. The resulted combined image is better in the contrast value because of the added edge points from the two combined images that depend on the suggested algorithms. This enhancement in edge regions is measured and reaches to double in enhancing the contrast. Different methods are used to be compared with the suggested method.

Image-based focus measurement in the manufacturing process is important for tasks such as automated inspection, automated assembly, and autosoldering. To inspect or manipulate an object, the visual system must sense the best image position to ensure that all the details of the product can be viewed. For this reason, an autofocus system is required. Traditional autofocus systems based on depth-from-focus (DFF) or depth-from-defocus (DFD) rely on a series of images by moving the lens with a control motor to find the sharpest location. The processing time defers it for in-line, real-time industrial applications. In this article, we propose a deep neural network regressor for fast and accurate autofocus. The convolutional neural network (CNN) model is proposed and evaluated for the autofocus task on a variety of electronic surfaces, including wafers, liquid crystal display (LCD), and ball grid array (BGA). The proposed deep regression model requires only one single sensed image of the object to estimate the depth. The effect of environmental variations in the illumination is evaluated for the robustness of the proposed method. Experimental results indicate the proposed CNN Regressor can achieve fast and accurate results for the tested objects with repetitive and arbitrary patterns. [ABSTRACT FROM AUTHOR]

For many parasitic diseases, the microscopic examination of clinical samples such as urine and stool still serves as the diagnostic reference standard, primarily because microscopes are accessible and cost-effective. However, conventional microscopy is laborious, requires highly skilled personnel, and is highly subjective. Requirements for skilled operators, coupled with the cost and maintenance needs of the microscopes, which is hardly done in endemic countries, presents grossly limited access to the diagnosis of parasitic diseases in resource-limited settings. The urgent requirement for the management of tropical diseases such as schistosomiasis, which is now focused on elimination, has underscored the critical need for the creation of access to easy-to-use diagnosis for case detection, community mapping, and surveillance. In this paper, we present a low-cost automated digital microscope—the Schistoscope—which is capable of automatic focusing and scanning regions of interest in prepared microscope slides, and automatic detection of Schistosoma haematobium eggs in captured images. The device was developed using widely accessible distributed manufacturing methods and off-the-shelf components to enable local manufacturability and ease of maintenance. For proof of principle, we created a Schistosoma haematobium egg dataset of over 5000 images captured from spiked and clinical urine samples from field settings and demonstrated the automatic detection of Schistosoma haematobium eggs using a trained deep neural network model. The experiments and results presented in this paper collectively illustrate the robustness, stability, and optical performance of the device, making it suitable for use in the monitoring and evaluation of schistosomiasis control programs in endemic settings.

Copyright of Baghdad Science Journal is the property of Republic of Iraq Ministry of Higher Education & Scientific Research (MOHESR) and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Using a multiple-input-multiple-output (MIMO) radar for environment sensing is gaining more attention in unmanned ground vehicles (UGV). During the movement of the UGV, the position of MIMO array compared to the ideal imaging position will inevitably change. Although compressed sensing (CS) imaging can provide high resolution imaging results and reduce the complexity of the system, the inaccurate MIMO array elements position will lead to defocusing of imaging. In this paper, a method is proposed to realize MIMO array motion error compensation and sparse imaging simultaneously. It utilizes a block coordinate descent (BCD) method, which iteratively estimates the motion errors of the transmitting and receiving elements, as well as synchronously achieving the autofocus imaging. The method accurately estimates and compensates for the motion errors of the transmitters and receivers, rather than approximating them as phase errors in the data. The validity of the proposed method is verified by simulation and measured experiments in a smoky environment.

In tomographic imaging, anatomical structures are reconstructed by applying a pseudo-inverse forward model to acquired signals. Geometric information within this process is usually depending on the system setting only, i.e., the scanner position or readout direction. Patient motion therefore corrupts the geometry alignment in the reconstruction process resulting in motion artifacts. We propose an appearance learning approach recognizing the structures of rigid motion independently from the scanned object. To this end, we train a siamese triplet network to predict the reprojection error (RPE) for the complete acquisition as well as an approximate distribution of the RPE along the single views from the reconstructed volume in a multi-task learning approach. The RPE measures the motion-induced geometric deviations independent of the object based on virtual marker positions, which are available during training. We train our network using 27 patients and deploy a 21-4-2 split for training, validation and testing. In average, we achieve a residual mean RPE of 0.013mm with an inter-patient standard deviation of 0.022mm. This is twice the accuracy compared to previously published results. In a motion estimation benchmark the proposed approach achieves superior results in comparison with two state-of-the-art measures in nine out of twelve experiments. The clinical applicability of the proposed method is demonstrated on a motion-affected clinical dataset. [ABSTRACT FROM AUTHOR]

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By phase‐modulating ring Airy Gaussian beams, ring Airy‐like beams propagating along predesigned parabolic trajectories are presented which combine the properties of accelerating beams and abruptly autofocusing beams analytically and numerically for the first time. The enhancement of the quadratic term ratio α shortens the autofocus distance and increases the slope of the beams after autofocusing. Interestingly, the main lobe tends to break into pieces as α increases and the possible reasons have been discussed. Furthermore, the distribution factor β and the radius of the primary r0 can prominently affect the autofocus distance and the intensity at the focal point but do not change the slope of the beams after the autofocusing. In addition, the self‐healing properties are validated to be retainable while RAiG beams via predesigned parabolic trajectories with various α. [ABSTRACT FROM AUTHOR]

In this article, we develop a polar format-based algorithm for recovering synthetic aperture radar (SAR) imagery from sparsely/randomly sampled data in the presence of motion compensation/phase errors. Our method integrates conventional SAR autofocus algorithms into an l1 norm penalized image recovery problem. We demonstrate the effectiveness of this approach using a real airborne SAR data set covering a large scene. [ABSTRACT FROM AUTHOR]

The autofocus level of the microscope is one of the factors that affect the user's work efficiency. In this paper, in order to improve the efficiency of the search algorithm while ensuring the search accuracy, a focus search algorithm with the function of predicting the best focus position based on the SOM neural network is proposed. The search algorithm makes full use of the automatic clustering function of SOM neural network. First complete the training of the SOM neural network through a large number of samples. The trained SOM neural network can directly predict the best focus position of the lens based on the input sample data. The results show that the auto-focusing algorithm proposed in this paper has high stability, and the probability of successful auto-focusing is not less than 94%. In terms of clarity threshold selection, it should be based on the number of cells. As for the clarity curve, the curve of the energy spectrum method has obvious peaks and high sensitivity. In terms of the real-time nature of autofocus, the motor is a key factor restricting the focusing speed. [ABSTRACT FROM AUTHOR]

This study proposed the concept of sparse and low-rank matrix decomposition to address the need for aviator's night vision goggles (NVG) automated inspection processes when inspecting equipment availability. First, the automation requirements include machinery and motor-driven focus knob of NVGs and image capture using cameras to achieve autofocus. Traditionally, passive autofocus involves first computing of sharpness of each frame and then use of a search algorithm to quickly find the sharpest focus. In this study, the concept of sparse and low-rank matrix decomposition was adopted to achieve autofocus calculation and image fusion. Image fusion can solve the multifocus problem caused by mechanism errors. Experimental results showed that the sharpest image frame and its nearby frame can be image-fused to resolve minor errors possibly arising from the image-capture mechanism. In this study, seven samples and 12 image-fusing indicators were employed to verify the image fusion based on variance calculated in a discrete cosine transform domain without consistency verification, with consistency verification, structure-aware image fusion, and the proposed image fusion method. Experimental results showed that the proposed method was superior to other methods and compared the autofocus put forth in this paper and the normalized gray-level variance sharpness results in the documents to verify accuracy. [ABSTRACT FROM AUTHOR]

Existing airborne SAR autofocus methods can be classified as parametric and non-parametric. Generally, non-parametric methods, such as the widely used phase gradient autofocus (PGA) algorithm, are only suitable for scenes with many dominant point targets, while the parametric ones are suitable for all types of scenes, in theory, but their efficiency is generally low. In practice, whether many dominant point targets are present in the scene is usually unknown, so determining what kind of algorithm should be selected is not straightforward. To solve this issue, this article proposes an airborne SAR autofocus approach combined with blurry imagery classification to improve the autofocus efficiency for ensuring autofocus precision. In this approach, we embed the blurry imagery classification based on a typical VGGNet in a deep learning community into the traditional autofocus framework as a preprocessing step before autofocus processing to analyze whether dominant point targets are present in the scene. If many dominant point targets are present in the scene, the non-parametric method is used for autofocus processing. Otherwise, the parametric one is adopted. Therefore, the advantage of the proposed approach is the automatic batch processing of all kinds of airborne measured data.