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An automatic focusing system is a crucial component of automated microscopes, adjusting the lens-to-object distance to find the optimal focus by maximizing the focus measure (FM) value. This study develops reliable autofocus methods for hyperspectral imaging microscope systems, essential for extracting accurate chemical and spatial information from hyperspectral datacubes. Since FMs are domain- and application-specific, commonly, their performance is evaluated using verified focus positions. For example, in optical microscopy, the sharpness/contrast of visual peculiarities of a sample under testing typically guides as an anchor to determine the best focus position, but this approach is challenging in hyperspectral imaging systems (HSISs), where instant two-dimensional hyperspectral images do not always possess human-comprehensible visual information. To address this, a principal component analysis (PCA) was used to define the optimal ("ideal") optical focus position in HSIS, providing a benchmark for assessing 22 FMs commonly used in other imaging fields. Evaluations utilized hyperspectral images from visible (400–1100 nm) and near-infrared (900–1700 nm) bands across four different HSIS setups with varying magnifications. Results indicate that gradient-based FMs are the fastest and most reliable operators in this context. [ABSTRACT FROM AUTHOR]

In the field of autofocus for optical systems, although passive focusing methods are widely used due to their cost-effectiveness, fixed focusing windows and evaluation functions in certain scenarios can still lead to focusing failures. Additionally, the lack of datasets limits the extensive research of deep learning methods. In this work, we propose a neural network autofocus method with the capability of dynamically selecting the region of interest (ROI). Our main work is as follows: first, we construct a dataset for automatic focusing of grayscale images; second, we transform the autofocus issue into an ordinal regression problem and propose two focusing strategies: full-stack search and single-frame prediction; and third, we construct a MobileViT network with a linear self-attention mechanism to achieve automatic focusing on dynamic regions of interest. The effectiveness of the proposed focusing method is verified through experiments, and the results show that the focusing MAE of the full-stack search can be as low as 0.094, with a focusing time of 27.8 ms, and the focusing MAE of the single-frame prediction can be as low as 0.142, with a focusing time of 27.5 ms. [ABSTRACT FROM AUTHOR]

Active vision systems (AVSs) have been widely used to obtain high-resolution images of objects of interest. However, tracking small objects in high-magnification scenes is challenging due to shallow depth of field (DoF) and narrow field of view (FoV). To address this, we introduce a novel high-speed AVS with a continuous autofocus (C-AF) approach based on dynamic-range focal sweep and a high-frame-rate (HFR) frame-by-frame tracking pipeline. Our AVS leverages an ultra-fast pan-tilt mechanism based on a Galvano mirror, enabling high-frequency view direction adjustment. Specifically, the proposed C-AF approach uses a 500 fps high-speed camera and a focus-tunable liquid lens operating at a sine wave, providing a 50 Hz focal sweep around the object's optimal focus. During each focal sweep, 10 images with varying focuses are captured, and the one with the highest focus value is selected, resulting in a stable output of well-focused images at 50 fps. Simultaneously, the object's depth is measured using the depth-from-focus (DFF) technique, allowing dynamic adjustment of the focal sweep range. Importantly, because the remaining images are only slightly less focused, all 500 fps images can be utilized for object tracking. The proposed tracking pipeline combines deep-learning-based object detection, K-means color clustering, and HFR tracking based on color filtering, achieving 500 fps frame-by-frame tracking. Experimental results demonstrate the effectiveness of the proposed C-AF approach and the advanced capabilities of the high-speed AVS for magnified object tracking. [ABSTRACT FROM AUTHOR]

In a fluorescence microscope, passive autofocus processing provides the best-focused cellular image in optical visualization during image collection. The steep focus curve is essential to high throughput microscopy for the application of cell images by reducing the number of stack image acquisitions to be focus-measured. The proposed scheme combines the non-parametric blur ratio computed by the two-step Otsu's threshold method to region of interest (ROI)-based autofocus measures based on the conventional sharpness metrics. The blur ratio expedites the gentle focus curve caused by arbitrary plane selection of an out-focused image to be segmented among stack images. It makes up for irregular focus computation by the improper threshold value of Otsu's method by bright big cells and epithelial debris. Also, the proposed blurriness curve of stack images identifies coarse and fine search areas in variable step-sized autofocus search algorithms, which decreases the number of image collection for calculating the focus metrics. In computer simulations with nuclei cell images containing U2OS cells stained with Hoechst from the Broad Bioimage Benchmark Collection (BBBC), the proposed autofocus algorithm yields superior sharpness performance of focus curve over the schemes applying the entire image and using focus ROI by Otsu's method. Four types of cellular fluorescence images are evaluated in terms of normalized focus curves and quantitative criteria of steepness, sharpness ratio, and sensitivity. Additionally, the performance excellence of the proposed autofocus algorithm is assessed by testing the rule-based search algorithm with a varying search size and adapting the blurriness curve in terms of the quantitation of autofocus speed (number of stack image acquisitions) and accuracy of focus position (not to lose best focus position). [ABSTRACT FROM AUTHOR]

Mini–unmanned aerial vehicles (mini-UAVs) are emerging as a promising platform for through-wall radar to sense the enclosed space in cities, especially high-rise buildings, due to their excellent maneuverability. However, due to unavoidable environmental interference such as airflow, mini-UAVs are prone to trajectory deviation thus degrading their sensing accuracy. Most of the existing approaches model the impact of trajectory deviation into a polynomial phase error on the received signal, which cannot fit the space-variant motion error well. Moreover, the large trajectory deviations of UAVs introduce the unavoidable envelope error. This article proposes an autofocusing algorithm based on the back projection (BP) image, which directly estimates the trajectory deviations between the actual and measured track. Thus, the problem of the 2D space variability of the motion error can be circumvented. The proposed method mainly consists of two steps. First, we estimate the trajectory deviation in the line-of-sight (LOS) direction by exploring the underlying linear property of the wall embedded in the BP imaging result. Then, the estimated trajectory deviation in the LOS direction is compensated for to obtain an updated BP image, followed by a Particle Swarm Optimization (PSO) approach to estimate the trajectory deviation along the track through focusing targets behind the wall. Simulations and practical experiments show that the proposed algorithm can accurately estimate the serious trajectory deviations larger than the range resolution, improving the sensing robustness of UAV-borne through-wall radar greatly. [ABSTRACT FROM AUTHOR]

Microscopes are indispensable devices of laboratories. They are widely used in industry and science, such as medicine, geology, biology, chemistry and so on. Thanks to the developing technology, manual microscopes are leaving their place to automatic systems. However, automatic microscopy systems are difficult to obtain due to their high costs. The best way to circumvent this problem is to reduce device costs as much as possible. Based on this idea, a fully automated digital microscope system (FADMS) has been proposed as a low-cost prototype. The FADMS can scan and autofocus for various microscopic samples. In addition, it can be controlled over the internet thanks to a developed software and can store scanned microscopic images. The total cost of the developed system is around 2500 US dollars. In experimental studies, mechanical motion sensitivity and focusing tests of the FADMS were performed. Five different methods were tested on peripheral blood smear images for autofocus. According to the results obtained based on six different measurement criteria, Brenner's and Geusebroek's method showed the best performance. In positioning tests for the mechanical stage (X and Y axes), the motors in the driving system were moved forward and backward for a distance of 100 μm. The results obtained showed a deviation of 2.6 μm for the X-axis and 3.6 μm for the Y-axis. Experimental results show that micron-sized biological cells can be observed in detail. The FADMS has been designed in a modular structure that allows it to be replaced with lighting, optical system and imaging device alternatives. In terms of performance/cost ratio, the FADMS is attractive for high-throughput microscopy applications ranging from digital pathology to health screening in low-income countries and is considered to be an alternative solution for many industries. [ABSTRACT FROM AUTHOR]

Given its capacity to generate 2-D fine images of observation areas, very high resolution (VHR) synthetic aperture radar (SAR) has become increasingly popular in myriad fields, including remote sensing, geoscience, and surveillance. In this article, we present a comprehensive overview of VHR SAR systems and imaging to highlight the related research and promote further development. First, we provide a summary of VHR SAR applications and summarize the typical airborne and spaceborne systems. Subsequently, a detailed overview is provided to demonstrate the range VHR technologies by introducing dechirp processing, stepped frequency, and microwave photonic. In addition, VHR imaging methods on different platforms, i.e., ideal trajectory, airborne, and spaceborne, are reviewed. Finally, the challenges and prospects of VHR SAR are presented to inform future research directions. This work provides a valuable resource to improve the system design and signal processing of VHR SAR.

The demand for drone-based synthetic aperture radar (SAR) systems is growing, especially for applications in cases where satellites or airborne systems are not sufficiently flexible. However, the combination of a long operating range and high spatial resolution causes the atmosphere to pose challenges for these systems. In this article, we present our $K$-band drone system that has a long range sensibility through modification to a commercially available radar. In addition, a moving baseline configuration has been integrated to ensure accurate attitude data, especially the heading. The desired spatial resolution is achieved by our proposed autofocus algorithm based on image sharpness. It is designed to also work in challenging cases of nonlinear flight paths and can be integrated into a processing framework based on back-projection. Several experiments were conducted to demonstrate the drone system's capabilities. These included a comparison with an established airborne radar: MIRANDA35. The obtained results demonstrate the ability of the drone SAR system to map wide areas at high spatial resolution.

An automatic focusing system is a crucial component of automated microscopes, adjusting the lens-to-object distance to find the optimal focus by maximizing the focus measure (FM) value. This study develops reliable autofocus methods for hyperspectral imaging microscope systems, essential for extracting accurate chemical and spatial information from hyperspectral datacubes. Since FMs are domain- and application-specific, commonly, their performance is evaluated using verified focus positions. For example, in optical microscopy, the sharpness/contrast of visual peculiarities of a sample under testing typically guides as an anchor to determine the best focus position, but this approach is challenging in hyperspectral imaging systems (HSISs), where instant two-dimensional hyperspectral images do not always possess human-comprehensible visual information. To address this, a principal component analysis (PCA) was used to define the optimal (“ideal”) optical focus position in HSIS, providing a benchmark for assessing 22 FMs commonly used in other imaging fields. Evaluations utilized hyperspectral images from visible (400–1100 nm) and near-infrared (900–1700 nm) bands across four different HSIS setups with varying magnifications. Results indicate that gradient-based FMs are the fastest and most reliable operators in this context.

As miniaturization technology has progressed, Synthetic Aperture Radar (SAR) can now be mounted on Unmanned Aerial Vehicles (UAVs) to carry out observational tasks. Influenced by airflow, UAVs inevitably experience deviations or vibrations during flight. In the context of cost constraints, the precision of the measurement equipment onboard UAVs may be relatively low. Nonetheless, high-resolution imaging demands more accurate track information. It is therefore of great importance to estimate high-precision tracks in the presence of both motion and measurement errors. This paper presents a robust track estimation method for airborne SAR that makes use of both envelope and phase errors. Firstly, weak navigation information is employed for motion compensation, which reduces a significant portion of the motion error. Subsequently, the track is initially estimated using additional envelope errors introduced by the Extended Omega-K (EOK) algorithm. The track is then refined using a phase-based approach. Furthermore, this paper presents the calculation method of the compensated component for each target and provides an analysis of accuracy from both theoretical and simulation perspectives. The track estimation and imaging results in the simulations and real data experiments validate the effectiveness of the proposed method, with an estimation accuracy of real data experiments within 5 cm. [ABSTRACT FROM AUTHOR]

The demand for drone-based synthetic aperture radar (SAR) systems is growing, especially for applications in cases where satellites or airborne systems are not sufficiently flexible. However, the combination of a long operating range and high spatial resolution causes the atmosphere to pose challenges for these systems. In this article, we present our $K$ -band drone system that has a long range sensibility through modification to a commercially available radar. In addition, a moving baseline configuration has been integrated to ensure accurate attitude data, especially the heading. The desired spatial resolution is achieved by our proposed autofocus algorithm based on image sharpness. It is designed to also work in challenging cases of nonlinear flight paths and can be integrated into a processing framework based on back-projection. Several experiments were conducted to demonstrate the drone system's capabilities. These included a comparison with an established airborne radar: MIRANDA35. The obtained results demonstrate the ability of the drone SAR system to map wide areas at high spatial resolution. [ABSTRACT FROM AUTHOR]

Featured Application: In this study, we report on fast and robust autofocus for non-mydriatic fundus photography under less stringent imaging conditions. Based on image sharpness evaluations, the adaptive focus window and path-optimized search are used to detect the focus position accurately. This method can be applied to fundus cameras or similar ophthalmic instruments, effectively alleviating imaging challenges posed by patients with fixation difficulties. Non-mydriatic fundus photography (NMFP) plays a vital role in diagnosing eye diseases, with its performance primarily dependent on the autofocus process. However, even minor maloperations or eye micro-movements can compromise fundus imaging quality, leading to autofocus inaccuracy and a heightened risk of misdiagnosis. To enhance the autofocus performance in NMFP, a fast and robust fundus autofocus method with adaptive window and path-optimized search is proposed. In this method, the adaptive focus window is used to suppress irrelevant image contents and correct the sharpness curve, and the path-optimized search is constructed to overcome the curve's local extrema, in order to achieve rapid focus position convergence. This method was simulated and clinically studied with the self-developed autofocus system for NMFP. The results of 80 cases of human eye imaging show that, compared with similar autofocus methods, this method achieves a focus success rate of 90% with the least axial scanning, and can adapt to non-ideal imaging conditions such as pupil misalignment, eyelash occlusion, and nystagmus. [ABSTRACT FROM AUTHOR]

In traditional Stripmap SAR imaging, the platform motion error will bring the phase error in the azimuthal direction to the image, which will have a series of effects on the imaging quality. The traditional autofocus algorithm—Stripmap Phase Gradient Algorithm (SPGA)—can estimate any order phase error above the second order in theory, but it is difficult to estimate the linear phase error, which leads to the discontinuity of the estimated phase error. It usually needs multiple iterations to focus an image, which is inefficient. Moreover, because the linear phase error cannot be estimated, the traditional SPGA cannot eliminate the target offset in the image, resulting in the distortion of the image in the azimuthal direction. According to the continuity of phase error, we propose a modified iteration-free SPGA based on removing the linear phase. Without iteration, the proposed autofocus algorithm can achieve comparable or even better results than traditional SPGA. In the simulation experiments, piecewise linear errors are added to the images of multiple targets. SPGA still fails to focus the image after six iterations. The average ILSR and ILSR are −7.11 dB and −3.99 dB, respectively, and the average number of point target drift is 8.42 pixels. The proposed algorithm optimizes the average ILSR and ILSR to −12.34 dB and −9.87 dB and reduces the average number of point target drift to 0.16 pixels. In the actual data processing, using image entropy as the evaluation criterion, the time consumption is only 19.25% of SPGA under the condition of achieving the same focusing quality. [ABSTRACT FROM AUTHOR]

This study proposes a modified optical design to improve the issue of autofocus accuracy in existing optical systems. The proposed system uses lens offset to convert incident light into non-parallel light, achieving a focus shift and avoiding severe deformation of the light spot near the focal point of the objective lens. Based on triangulation theory and optical focusing theories such as the centroid method, the proposed optical design improves the shortcomings of existing technology. Experimental results demonstrate that the proposed optical autofocusing system has better autofocus accuracy than traditional systems while also reducing the difficulty of image processing. In summary, the proposed optical system is not only an effective autofocusing technology but also a highly valuable optical inspection and industrial application technology. This system has broader application and development opportunities for future research and practice. [ABSTRACT FROM AUTHOR]

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In the field of autofocus for optical systems, although passive focusing methods are widely used due to their cost-effectiveness, fixed focusing windows and evaluation functions in certain scenarios can still lead to focusing failures. Additionally, the lack of datasets limits the extensive research of deep learning methods. In this work, we propose a neural network autofocus method with the capability of dynamically selecting the region of interest (ROI). Our main work is as follows: first, we construct a dataset for automatic focusing of grayscale images; second, we transform the autofocus issue into an ordinal regression problem and propose two focusing strategies: full-stack search and single-frame prediction; and third, we construct a MobileViT network with a linear self-attention mechanism to achieve automatic focusing on dynamic regions of interest. The effectiveness of the proposed focusing method is verified through experiments, and the results show that the focusing MAE of the full-stack search can be as low as 0.094, with a focusing time of 27.8 ms, and the focusing MAE of the single-frame prediction can be as low as 0.142, with a focusing time of 27.5 ms.

Active vision systems (AVSs) have been widely used to obtain high-resolution images of objects of interest. However, tracking small objects in high-magnification scenes is challenging due to shallow depth of field (DoF) and narrow field of view (FoV). To address this, we introduce a novel high-speed AVS with a continuous autofocus (C-AF) approach based on dynamic-range focal sweep and a high-frame-rate (HFR) frame-by-frame tracking pipeline. Our AVS leverages an ultra-fast pan-tilt mechanism based on a Galvano mirror, enabling high-frequency view direction adjustment. Specifically, the proposed C-AF approach uses a 500 fps high-speed camera and a focus-tunable liquid lens operating at a sine wave, providing a 50 Hz focal sweep around the object’s optimal focus. During each focal sweep, 10 images with varying focuses are captured, and the one with the highest focus value is selected, resulting in a stable output of well-focused images at 50 fps. Simultaneously, the object’s depth is measured using the depth-from-focus (DFF) technique, allowing dynamic adjustment of the focal sweep range. Importantly, because the remaining images are only slightly less focused, all 500 fps images can be utilized for object tracking. The proposed tracking pipeline combines deep-learning-based object detection, K-means color clustering, and HFR tracking based on color filtering, achieving 500 fps frame-by-frame tracking. Experimental results demonstrate the effectiveness of the proposed C-AF approach and the advanced capabilities of the high-speed AVS for magnified object tracking.

'openFrame' is a modular, low‐cost, open‐hardware microscopy platform that can be configured or adapted to most light microscopy techniques and is easily upgradeable or expandable to multiple modalities. The ability to freely mix and interchange both open‐source and proprietary hardware components or software enables low‐cost, yet research‐grade instruments to be assembled and maintained. It also enables rapid prototyping of advanced or novel microscope systems. For long‐term time‐lapse image data acquisition, slide‐scanning or high content analysis, we have developed a novel optical autofocus incorporating orthogonal cylindrical optics to provide robust single‐shot closed‐loop focus lock, which we have demonstrated to accommodate defocus up to ±37 μm with <200 nm accuracy, and a two‐step autofocus mode which we have shown can operate with defocus up to ±68 μm. We have used this to implement automated single molecule localisation microscopy (SMLM) in a relatively low‐cost openFrame‐based instrument using multimode diode lasers for excitation and cooled CMOS cameras. LAY DESCRIPTION: We present a modular, low‐cost, microscopy platform called 'openFrame' that can be configured or adapted to most light microscopy techniques and is easily upgradeable. openFrame‐based microscopes are designed to be used with open‐source software tools and low‐cost/self‐built hardware components, such that cost‐effective instruments can be assembled and maintained locally by skilled users instead of requiring visits from service engineers. The core openFrame hardware components can be fabricated using freely shared CAD files or they can be purchased by users to implement a wide range of instruments, which can utilise a mixture of open and proprietary hardware components as desired. We intend this platform to widen access to basic and advanced microscopy techniques, including in lower‐resource settings. We also expect it to be useful to researchers prototyping new microscopes, since it allows a core instrument to be rapidly assembled and then easily modified with minimal constraints, for example, from proprietary software or legacy design choices. The flexibility to reconfigure openFrame‐based microscopes and interchange/recycle components between different instruments reduces obsolescence. The openFrame‐based microscope presented here includes a novel open‐source optical autofocus unit (openAF) that may be useful for long‐term time‐lapse imaging, and in slide‐scanning or automated multiwell plate microscopes. We demonstrate the use of an openFrame‐based instrument for super‐resolved microscopy using single molecule localisation microscopy (SMLM) utilising low‐cost cooled CMOS cameras and multimode diode lasers for high power excitation. [ABSTRACT FROM AUTHOR]

Turbulent fluctuations of the electron number density in the Earth's ionosphere may hamper the performance of spaceborne synthetic aperture radar (SAR). Previously, we have quantified the extent of the possible degradation of transionospheric SAR images as it depends on the state of the ionosphere and parameters of the SAR instrument. Yet no attempt has been made to mitigate the adverse effect of the ionospheric turbulence. In the current work, we propose a new optimizationbased autofocus algorithm that helps correct the turbulence-induced distortions of spaceborne SAR images. Unlike the traditional autofocus procedures available in the literature, the new algorithm allows for the dependence of the phase perturbations of SAR signals not only on slow time but also on the target coordinates. This dependence is central for the analysis of image distortions due to turbulence, but in the case of traditional autofocus where the distortions are due to uncertainties in the antenna position, it is not present. [ABSTRACT FROM AUTHOR]

Mini–unmanned aerial vehicles (mini-UAVs) are emerging as a promising platform for through-wall radar to sense the enclosed space in cities, especially high-rise buildings, due to their excellent maneuverability. However, due to unavoidable environmental interference such as airflow, mini-UAVs are prone to trajectory deviation thus degrading their sensing accuracy. Most of the existing approaches model the impact of trajectory deviation into a polynomial phase error on the received signal, which cannot fit the space-variant motion error well. Moreover, the large trajectory deviations of UAVs introduce the unavoidable envelope error. This article proposes an autofocusing algorithm based on the back projection (BP) image, which directly estimates the trajectory deviations between the actual and measured track. Thus, the problem of the 2D space variability of the motion error can be circumvented. The proposed method mainly consists of two steps. First, we estimate the trajectory deviation in the line-of-sight (LOS) direction by exploring the underlying linear property of the wall embedded in the BP imaging result. Then, the estimated trajectory deviation in the LOS direction is compensated for to obtain an updated BP image, followed by a Particle Swarm Optimization (PSO) approach to estimate the trajectory deviation along the track through focusing targets behind the wall. Simulations and practical experiments show that the proposed algorithm can accurately estimate the serious trajectory deviations larger than the range resolution, improving the sensing robustness of UAV-borne through-wall radar greatly.

The polygonal multifocal propagation properties and particle trapping experiments of a new kind of power‐exponential‐phase vortex chirped circular Pearcey Gaussian beam are investigated. Theoretical and experimental results show that it is possible to adjust the position and transverse spatial distribution of the beam in focal plane, as well as to make the transverse intensity distribution emerge an arbitrary polygonal profile by changing the distribution factor and vortex topological charge. Stable trapping of multiple particles is achieved in the experiment with transverse intensity fields in the polygon plane with different topological charges. [ABSTRACT FROM AUTHOR]

This paper presents an examination study of 11 autofocus algorithms for printed circuit board (PCB) automated optical inspection (AOI). A selection of an optimal algorithm for that application based on some criteria was carried out. Unlike microscopy, PCB optical inspection does not require very high magnification. The object in this work was also different from that of microscopy and thus influenced the image features. We analyzed 47 PCB images, size of 640×480, sequentially captured every 1 mm in the z-direction. This work utilized USB digital microscope, and the magnification was set at ten times. Each algorithm calculated the sharpness values of the image sequences, and the plot of the sharpness profile was created. Moreover, the research also carried out experiments in several strategies, including image resizing and applying the non-local means (NLM) denoising filter to assess the algorithm performance in different situations. The algorithms were examined and ranked based on five criteria, i.e., computation time, full width at half maximum (FWHM), accuracy, number of half maxima, and range. The experimentation results showed that the Brenner gradient worked best for analyzing images both in their original dimension or resized images. [ABSTRACT FROM AUTHOR]

Piezoelectric objective driver positioners are increasingly used in the field of microscopy. They have the advantages of high dynamic and fast response. This paper presents a fast autofocus algorithm for highly interactive microscope system. First, the Tenengrad gradient of the down‐sampled image is used to calculate the image sharpness, and Brent search method is adopted to quickly converge to the correct focal length. At the same time, the input shaping method is used to eliminate the displacement vibration of the piezoelectric objective lens driver and further accelerate the image acquisition speed. Experimental results show that the proposed scheme can improve the speed of the automatic focusing task of the piezoelectric objective driver and improve the real‐time focus of the automatic microscopic system. Highlights: A high real‐time autofocus strategy.A vibration control method suitable for a piezoelectric objective driver. [ABSTRACT FROM AUTHOR]

Circular synthetic aperture radar (CSAR) can obtain higher image resolution and more target information through 360° observation of the target. However, CSAR is often subject to motion errors due to the sensor's accuracy limitations and the unique trajectory of the system. The autofocusing back projection algorithm based on the optimization of image sharpness compensates for motion errors through phase-error estimation. This method can attain relatively good performance while assuming the same error for all pixels; it ignores the spatial variance of motion errors. In order to solve this problem, this paper proposes an autofocusing method based on the Prewitt operator and particle swarm optimization (PSO), which first extracts the subaperture image feature points as a new dataset using the Prewitt operator, estimates the radar slope error on the new dataset using PSO, and subsequently compensates the subaperture image. Finally, the subaperture images are synthesized using an image-alignment method. The results of both the simulated experiments and the measured data validate the effectiveness of this method. [ABSTRACT FROM AUTHOR]

The radar echo signal may experience substantial two-dimensional spatial variance due to the nonlinear Synthetic Aperture Radar (SAR) trajectory. The traditional frequency-domain imaging algorithms based on the assumption of azimuth translational invariance are unsuitable for high-precision imaging of nonlinear trajectory SAR. Therefore, for nonlinear trajectory SAR imaging, the azimuth spatial variance of the echo signals is typically rectified using complex Nonlinear Chirp Scaling (NCS). However, when there are substantial motion errors, it cannot be effectively combined with the current autofocus algorithms considering the complexity of the algorithm due to too many NCS parameters. Thus, to address this issue, this study proposes a nonlinear trajectory SAR imaging and autofocus method according to the sub-image NCS, which can reduce the number of NCS parameters and ensure imaging accuracy; moreover, it is more conducive to the subsequent autofocus processing. The effectiveness of the suggested approach is confirmed by simulation and measured data processing.

Long synthetic aperture time and large instantaneous beam can turn airborne synthetic aperture radar (SAR) autofocus into a wide-beam autofocus problem; i.e., the motion error is both range- and azimuth-spatial variant. A typical two-step MoCo method cannot process wide-beam airborne SAR data. Moreover, traditional wide-beam SAR MoCo algorithms, such as sub-aperture topography and aperture-dependent (SATA), precise topography- and aperture-dependent (PTA), and frequency division (FD), are highly dependent on high-precision inertial navigation system/global positioning system (INS/GPS) data and belong to the sub-aperture method, which may result in serious grant-lobe or stitching problems in the image. Alternatively, this article proposes a full-aperture autofocus method for wide-beam SAR based on blind RS. The proposed method does not require INS/GPS data and avoids the problems of traditional sub-aperture methods, which can significantly improve the overall image quality. The measured data processing results of the wide-beam SAR verify the effectiveness of the proposed algorithm. [ABSTRACT FROM AUTHOR]

Two-photon lithography (TPL) plays a vital role in microstructure fabrication due to its high processing accuracy and maskless characteristics. To optimize the manufacturing quality deteriorated by the defocus of the substrate, an autofocus approach based on improved differential confocal microscopy (IDCM) is proposed in this paper. Through analyzing the intensity response signals from two detectors with symmetrical axial offset, the defocus amount is measured and compensated for with high precision and noise immunity to stabilize the substrate. The verification experiments on the coverslip reported a detection sensitivity of 5 nm, a repetitive measurement accuracy of less than 15 nm, and a focusing accuracy reaching around 5 nm. The consistency between simulation and characterization demonstrated the effectiveness and superior performance of the autofocus system for the high production quality of the metalens array. The proposed autofocus method shows promise for further application to the fabrication of complex structures on various substrates. [ABSTRACT FROM AUTHOR]

As miniaturization technology has progressed, Synthetic Aperture Radar (SAR) can now be mounted on Unmanned Aerial Vehicles (UAVs) to carry out observational tasks. Influenced by airflow, UAVs inevitably experience deviations or vibrations during flight. In the context of cost constraints, the precision of the measurement equipment onboard UAVs may be relatively low. Nonetheless, high-resolution imaging demands more accurate track information. It is therefore of great importance to estimate high-precision tracks in the presence of both motion and measurement errors. This paper presents a robust track estimation method for airborne SAR that makes use of both envelope and phase errors. Firstly, weak navigation information is employed for motion compensation, which reduces a significant portion of the motion error. Subsequently, the track is initially estimated using additional envelope errors introduced by the Extended Omega-K (EOK) algorithm. The track is then refined using a phase-based approach. Furthermore, this paper presents the calculation method of the compensated component for each target and provides an analysis of accuracy from both theoretical and simulation perspectives. The track estimation and imaging results in the simulations and real data experiments validate the effectiveness of the proposed method, with an estimation accuracy of real data experiments within 5 cm.

Non-mydriatic fundus photography (NMFP) plays a vital role in diagnosing eye diseases, with its performance primarily dependent on the autofocus process. However, even minor maloperations or eye micro-movements can compromise fundus imaging quality, leading to autofocus inaccuracy and a heightened risk of misdiagnosis. To enhance the autofocus performance in NMFP, a fast and robust fundus autofocus method with adaptive window and path-optimized search is proposed. In this method, the adaptive focus window is used to suppress irrelevant image contents and correct the sharpness curve, and the path-optimized search is constructed to overcome the curve’s local extrema, in order to achieve rapid focus position convergence. This method was simulated and clinically studied with the self-developed autofocus system for NMFP. The results of 80 cases of human eye imaging show that, compared with similar autofocus methods, this method achieves a focus success rate of 90% with the least axial scanning, and can adapt to non-ideal imaging conditions such as pupil misalignment, eyelash occlusion, and nystagmus.

This study proposes a modified optical design to improve the issue of autofocus accuracy in existing optical systems. The proposed system uses lens offset to convert incident light into non-parallel light, achieving a focus shift and avoiding severe deformation of the light spot near the focal point of the objective lens. Based on triangulation theory and optical focusing theories such as the centroid method, the proposed optical design improves the shortcomings of existing technology. Experimental results demonstrate that the proposed optical autofocusing system has better autofocus accuracy than traditional systems while also reducing the difficulty of image processing. In summary, the proposed optical system is not only an effective autofocusing technology but also a highly valuable optical inspection and industrial application technology. This system has broader application and development opportunities for future research and practice.

In traditional Stripmap SAR imaging, the platform motion error will bring the phase error in the azimuthal direction to the image, which will have a series of effects on the imaging quality. The traditional autofocus algorithm—Stripmap Phase Gradient Algorithm (SPGA)—can estimate any order phase error above the second order in theory, but it is difficult to estimate the linear phase error, which leads to the discontinuity of the estimated phase error. It usually needs multiple iterations to focus an image, which is inefficient. Moreover, because the linear phase error cannot be estimated, the traditional SPGA cannot eliminate the target offset in the image, resulting in the distortion of the image in the azimuthal direction. According to the continuity of phase error, we propose a modified iteration-free SPGA based on removing the linear phase. Without iteration, the proposed autofocus algorithm can achieve comparable or even better results than traditional SPGA. In the simulation experiments, piecewise linear errors are added to the images of multiple targets. SPGA still fails to focus the image after six iterations. The average ILSR and ILSR are −7.11 dB and −3.99 dB, respectively, and the average number of point target drift is 8.42 pixels. The proposed algorithm optimizes the average ILSR and ILSR to −12.34 dB and −9.87 dB and reduces the average number of point target drift to 0.16 pixels. In the actual data processing, using image entropy as the evaluation criterion, the time consumption is only 19.25% of SPGA under the condition of achieving the same focusing quality.

The two-step approach (TSA) is an efficient full-aperture method to solve the Doppler spectrum aliasing when processing data acquired by a synthetic aperture radar (SAR) system operating in beam steering mode. This simple approach solves the spectral aliasing by implementing an azimuth convolution between the raw data and a chirp signal, thus avoiding complex subaperture division and recombination of subaperture approaches. However, as aliasing reoccurs in the slow time-domain after the convolution, the estimation and compensation for residual motion error by autofocus cannot be performed immediately, which is crucial for airborne and high-resolution spaceborne SAR processing. The extended TSA solves this problem by implementing full-aperture azimuth scaling, but it is inefficient when the scaling factor deviates from unity severely, which is common in the data acquisition geometry of an airborne SAR. Aiming at addressing the existing shortcomings, this article combines the TSA with autofocus by the time-domain dealiasing and provides an efficient full-aperture processing framework of airborne spotlight SAR data. Simulated and experimental airborne spotlight SAR data with the transmission signal bandwidth of 1.2 GHz and a coherent integration angle of 15$^\circ$ are processed by the proposed algorithm and the clarity of the processed results shows its effectiveness.

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. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

In optoacoustic (photoacoustic) tomography, several parameters related to tissue and detector features are needed for image formation, but they may not be known a priori. An autofocus (AF) algorithm is generally used to estimate these parameters. However, the algorithm works iteratively and is therefore impractical for clinical imaging with planar geometry systems due to the long reconstruction times. We have developed a fast autofocus (FAF) algorithm for 3D optoacoustic systems with planar geometry. Such an algorithm exploits the symmetries of the planar geometry and a virtual source concept to reduce the dimensionality of the parameter estimation problem. The dimensionality reduction makes FAF much simpler computationally than the conventional AF algorithm. We show that the FAF algorithm required about 5 s to provide accurate estimates of the speed of sound in simulated data and experimental data obtained using an imaging system that is poised to enter the clinic. The applicability of FAF for estimating other image formation parameters is discussed. We expect the FAF algorithm to contribute decisively to the clinical use of optoacoustic tomography systems with planar geometry. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

This article describes how to form a SAR image without proper motion quantities. That is within the scope of factorized geometrical autofocus (FGA). The FGA algorithm is a fast-factorized back-projection formulation with adjustable geometry parameters. Subapertures are tuned and merged pair-by-pair (base-2) and step-by-step. With this technique, we can correct an erroneous geometry and form a focused image. The FGA algorithm has been applied on two datasets, acquired by the UWB CARABAS 3 system at UHF-band. The tracks are measured accurately by means of a DGPS. We however adopt and modify a geometry model. Equidistant linear tracks at fixed altitudes are assumed. These tracks are then regulated via a two-stage search strategy and a reverseprocessing procedure. As this is a first experiment at UHF-band, we provide GPS-based length values for the subapertures, to simplify the search. Multiple geometry solutions are tested for each subaperture pair, i.e., at each resolution level. Resulting FGA images are compared to reference images and verified to be focused. This indicates that it is feasible to form a focused image with wavelength resolution at UHF-band, i.e., with minimum support from a motion measurement system. [ABSTRACT FROM AUTHOR]

This paper describes the implementation of an autofocus function for a laser beam in a high-speed, phase-sensitive laser probe system for RF surface/bulk acoustic wave devices. This implementation can compensate for defocus caused during continuous measurements that take dozens of hours. After a brief explanation of the system used in this work, a detailed discussion is given on the employed evaluation function indicating focus status, which is a key factor determining autofocus reliability. It is shown that the sum of the energies of Laplacians is suitable as an evaluation function, which can be calculated by an image of the probing laser spot captured by a built-in CCD camera. Then, the implementation of the autofocus function in the current system is detailed. It is confirmed that this function can adjust the focus within almost ± 20 μ m defocus conditions. Finally, it is confirmed how the implemented autofocus function works effectively to keep just-in-focus under disturbance. [ABSTRACT FROM AUTHOR]

Two-photon lithography (TPL) plays a vital role in microstructure fabrication due to its high processing accuracy and maskless characteristics. To optimize the manufacturing quality deteriorated by the defocus of the substrate, an autofocus approach based on improved differential confocal microscopy (IDCM) is proposed in this paper. Through analyzing the intensity response signals from two detectors with symmetrical axial offset, the defocus amount is measured and compensated for with high precision and noise immunity to stabilize the substrate. The verification experiments on the coverslip reported a detection sensitivity of 5 nm, a repetitive measurement accuracy of less than 15 nm, and a focusing accuracy reaching around 5 nm. The consistency between simulation and characterization demonstrated the effectiveness and superior performance of the autofocus system for the high production quality of the metalens array. The proposed autofocus method shows promise for further application to the fabrication of complex structures on various substrates.

Autofocus methods play crucial roles in optical systems, which closely relate to the collected image quality. Due to the different focal lengths of cervical areas in smears, existing focusing approaches often result in blurry images of lymphocytes and epithelial cells, which are the keys for the cervical cancer detection. Aiming at this problem, a novel focus method based on multi regions of interest window is presented. The proposed approach applies multiple-median filter and histogram equalization for the image denoising. Multi-ROI (Region of interest) focus window consisting of image processing, selective search and BP (Back Propagation) neural network is used for the autofocus. Comprehensive analysis denotes that the proposed autofocus algorithm achieves the accuracy of 93.7% and an average focusing time (sec/mm2) of 11.89. Validation on another dataset CC proves its robustness, comparison with the recent studies shows its practical performance. The results which we obtained suggest that the proposed autofocus model based on multi-ROI window can be used effectively in scanning of cervical cell images. [ABSTRACT FROM AUTHOR]

For ultrahigh resolution synthetic aperture radar (SAR), azimuth spectrum aliasing limits the application of frequency-domain autofocus algorithms. Therefore, time-domain autofocus algorithms are often used for ultrahigh resolution SAR imaging. However, the azimuth deramping operation in current time-domain autofocus algorithms may introduce an additional azimuth-dependent phase. This phase can be regarded as a part of the phase error, which significantly reduces the estimation accuracy of the phase error. To address this issue, this article proposes a new time-domain autofocus algorithm based on azimuth scaling transformation (AST) for ultrahigh resolution SAR. In this algorithm, we first adopt the azimuth scaling operation to avoid the azimuth-dependent phase so that the estimation accuracy of error can be greatly improved. Then, for the azimuth-dependent shifts caused by the azimuth scaling operation, we adopt the alignment processing to remove them in azimuth-time domain. Finally, we can estimate the error accurately from the aligned signal. The simulation and measured data were processed to verify the effectiveness of the algorithm. [ABSTRACT FROM AUTHOR]

Synthetic aperture radar (SAR) can provide high-resolution electromagnetic backscattering images of the illuminated area, playing a significant role in various applications. However, achieving focused SAR images is challenging under sparse sampling and phase error conditions. By exploiting the sparsity or compressibility priors, the state-of-the-art sparsity-driven SAR imaging methods can reconstruct images under the condition of sparse sampling. However, the handcrafted priors used in these methods limit the imaging performance, and the iterative solution schemes reduce the computational efficiency. Besides, the measurement inaccuracy introduced by the phase error also degrades the reconstruction performance of the sparsity-driven imaging methods. To address these issues, a deep network for SAR autofocus imaging is proposed, which alternately performs image reconstruction and phase error estimation. When performing image reconstruction, the sparsity-cognizant total least-square (S-TLS) model is introduced to handle the problem of measurement inaccuracy, contributing to robust reconstruction performance under the condition of phase error. During the implementation of the deep network, a feature transform operator is used to realize data-driven prior knowledge learning and overcome the limitations of handcrafted priors. Moreover, the deep network approach can significantly improve computational efficiency. Experiments on simulated and real data verify the effectiveness and efficiency of the proposed method. [ABSTRACT FROM AUTHOR]