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Conventional microscopes designed for submicron resolution in biological research are hindered by a limited field of view, typically around 1 mm. This restriction poses a challenge when attempting to simultaneously analyze various parts of a sample, such as different brain areas. In addition, conventional objective lenses struggle to perform consistently across the required range of wavelengths for brain imaging in vivo. Here we present a novel mesoscopic objective lens with an impressive field of view of 8 mm, a numerical aperture of 0.5, and a working wavelength range from 400 to 1000 nm. We achieved a resolution of 0.74 μm in fluorescent beads imaging. The versatility of this lens was further demonstrated through high-quality images of mouse brain and kidney sections in a wide-field imaging system, a confocal laser scanning system, and a two-photon imaging system. This mesoscopic objective lens holds immense promise for advancing multi-wavelength imaging of large fields of view at high resolution.

Introduction: Pine wilt disease spreads rapidly, leading to the death of a large number of pine trees. Exploring the corresponding prevention and control measures for different stages of pine wilt disease is of great significance for its prevention and control. Methods: To address the issue of rapid detection of pine wilt in a large field of view, we used a drone to collect multiple sets of diseased tree samples at different times of the year, which made the model trained by deep learning more generalizable. This research improved the YOLO v4(You Only Look Once version 4) network for detecting pine wilt disease, and the channel attention mechanism module was used to improve the learning ability of the neural network. Results: The ablation experiment found that adding the attention mechanism SENet module combined with the self-designed feature enhancement module based on the feature pyramid had the best improvement effect, and the mAP of the improved model was 79.91%. Discussion: Comparing the improved YOLO v4 model with SSD, Faster RCNN, YOLO v3, and YOLO v5, it was found that the mAP of the improved YOLO v4 model was significantly higher than the other four models, which provided an efficient solution for intelligent diagnosis of pine wood nematode disease. The improved YOLO v4 model enables precise location and identification of pine wilt trees under changing light conditions. Deployment of the model on a UAV enables large-scale detection of pine wilt disease and helps to solve the challenges of rapid detection and prevention of pine wilt disease. [ABSTRACT FROM AUTHOR]

Mid-infrared large field-of-view (FOV) imaging optics play a vital role in infrared imaging and detection. The metalens, which is composed of subwavelength-arrayed structures, provides a new possibility for the miniaturization of large FOV imaging systems. However, the inaccuracy during fabrication is the main obstacle to developing practical uses for metalenses. Here, we introduce the principle and method of designing a large FOV doublet metalens at the mid-infrared band. Then, the quantitative relationship between the fabrication error and the performance of the doublet metalens with a large FOV from four different fabrication errors is explored by using the finite-difference time-domain method. The simulation results show that the inclined sidewall error has the greatest impact on the focusing performance, and the interlayer alignment error deforms the focusing beam and affects the focusing performance, while the spacer thickness error has almost no impact on the performance. The contents discussed in this paper can help manufacturers determine the allowable processing error range of the large FOV doublet metalens and the priority level for optimizing the process, which is of significance. [ABSTRACT FROM AUTHOR]

This study reported on the large field-of-view observable color code obtained by femtosecond laser-induced oxidation on titanium alloy. The simulation results showed that simultaneous roughening of the oxide layer and metal layer could effectively increase the color rendering range of the structural colors. Based on the diffraction effect of the diaphragm, the laser energy was periodically distributed. In addition, the simultaneous formation of the oxide layer and the micro/nano structures was attributed to the periodic distribution of laser energy. Compared to the substrate, the increase in oxygen content of the colored area ranged from 5 % to 11 %, indicating that the infiltration of oxygen inside the material was increased. Additionally, the surface chemical composition of the upper oxide layer of the colored areas was TiO 2. XRD analysis showed that laser-induced surface coloring did not affect the phase composition of the alloy except for the generation of a small amount of carbon. Moreover, different scanning numbers affected the color difference ΔE∗ ab of adjacent scanning speeds. High-temperature experiments showed that the color code prepared by femtosecond laser-induced oxidization of titanium alloy was reliable in use at less than 100 °C. Since the color code could still be clearly obtained at a field angle of 77.3°, the angle insensitivity of the color code could be effectively improved by selective roughening of the oxide layer prepared by femtosecond laser. Furthermore, the decoding of color code was the inverse encoding process. Therefore, the angle insensitivity of the color code was improved and the temperature range of the structural color was clarified. • The formation of the oxide layer and the micro/nano structures was attributed to the periodic distribution of laser energy. • The angle insensitivity of color code could be effectively improved by selective roughening of the oxide layer. • The color code prepared by femtosecond laser-induced oxidization of titanium alloy was reliable in use at less than 100 °C. [ABSTRACT FROM AUTHOR]

In terms of the global coverage provided by global navigation satellite system (GNSS) signals, radar images of a large field-of-view can be obtained via passive synthetic aperture radar (SAR) using these signals of opportunity. Usually, large field-of-view imaging is challenging using conventional SAR methods owing to the space-variant resolution. In addition, only a moderate range resolution can be provided by the GNSS transmitters in the passive SAR system. In this article, an optimal imaging method for a large field-of-view using GNSS-based passive SAR is proposed, which exploits the inherent multistatic nature of GNSS. At first, the spatial resolution of each scatterer in the observation area relative to each GNSS transmitter is calculated according to the geometry. Then, the selection of the transmitter set is modeled as an optimization problem, where the minimal resolution cell is used as the optimal criterion. After that, an evolutionary algorithm is applied to solve the optimization problem. Finally, the space-time combination of the echoes over a long dwell time concerning selected GNSS transmitters is performed to form the radar image with a minimal spatial resolution. The proposed method is extensively validated using two experimental datasets, where the passive radar consists of BeiDou satellites and a fixed receiver. The spatial resolution improvement and the large field-of-view imaging capability of the proposed method are forcefully demonstrated according to the experimental results.

Information in the brain is represented by the collective and coordinated activity of single neurons. Activity is determined by a large amount of dynamic synaptic inputs from neurons in the same and/or distant brain regions. Therefore, the simultaneous recording of single neurons across several brain regions is critical for revealing the interactions among neurons that reflect the computational principles of the brain. Recently, several wide-field two-photon (2P) microscopes equipped with sizeable objective lenses have been reported. These microscopes enable large-scale in vivo calcium imaging and have the potential to make a significant contribution to the elucidation of information-processing mechanisms in the cerebral cortex. This review discusses recent reports on wide-field 2P microscopes and describes the trade-offs encountered in developing wide-field 2P microscopes. Large-scale imaging of neural activity allows us to test hypotheses proposed in theoretical neuroscience, and to identify rare but influential neurons that have potentially significant impacts on the whole-brain system. • Trade-offs among FOV, optical resolution, frame rate, and SNR are obstacles in developing 2P microscopes. • Aberration-free large objective lenses break the trade-off, resulting in the development of fast and wide-field 2P microscopes. • Wide-field 2P microscopes achieve neural activity recordings across multiple brain regions. • Large-scale imaging enables testing hypotheses proposed in theoretical neuroscience. [ABSTRACT FROM AUTHOR]

Mid-infrared large field-of-view (FOV) imaging optics play a vital role in infrared imaging and detection. The metalens, which is composed of subwavelength-arrayed structures, provides a new possibility for the miniaturization of large FOV imaging systems. However, the inaccuracy during fabrication is the main obstacle to developing practical uses for metalenses. Here, we introduce the principle and method of designing a large FOV doublet metalens at the mid-infrared band. Then, the quantitative relationship between the fabrication error and the performance of the doublet metalens with a large FOV from four different fabrication errors is explored by using the finite-difference time-domain method. The simulation results show that the inclined sidewall error has the greatest impact on the focusing performance, and the interlayer alignment error deforms the focusing beam and affects the focusing performance, while the spacer thickness error has almost no impact on the performance. The contents discussed in this paper can help manufacturers determine the allowable processing error range of the large FOV doublet metalens and the priority level for optimizing the process, which is of significance.

Structured light three-dimensional (3D) imaging technology captures the geometric information on 3D objects by recording waves reflected from the objects' surface. The projection angle and point number of the laser dots directly determine the field-of-view (FOV) and the resolution of the reconstructed image. Conventionally, diffractive optical elements with micrometer-scale pixel size have been used to generate laser dot arrays, leading to limited FOV and point number within the projection optical path. Here, we theoretically put forward and experimentally demonstrate a monocular geometric phase metasurface composed of deep subwavelength meta-atoms to generate a 10 798 dot array within an FOV of 163°. Attributed to the vast number and high-density point cloud generated by the metasurface, the 3D reconstructed results showcase a maximum relative error in depth of 5.3 mm and a reconstruction error of 6.07%. Additionally, we propose a spin-multiplexed metasurface design method capable of doubling the number of lattice points. We demonstrate its application in the field of 3D imaging through experiments, where the 3D reconstructed results show a maximum relative depth error of 0.44 cm and a reconstruction error of 2.78%. Our proposed metasurface featuring advanced point cloud generation holds substantial potential for various applications such as facial recognition, autonomous driving, virtual reality, and beyond.

Introduction: Pine wilt disease spreads rapidly, leading to the death of a large number of pine trees. Exploring the corresponding prevention and control measures for different stages of pine wilt disease is of great significance for its prevention and control., Methods: To address the issue of rapid detection of pine wilt in a large field of view, we used a drone to collect multiple sets of diseased tree samples at different times of the year, which made the model trained by deep learning more generalizable. This research improved the YOLO v4(You Only Look Once version 4) network for detecting pine wilt disease, and the channel attention mechanism module was used to improve the learning ability of the neural network., Results: The ablation experiment found that adding the attention mechanism SENet module combined with the self-designed feature enhancement module based on the feature pyramid had the best improvement effect, and the mAP of the improved model was 79.91%., Discussion: Comparing the improved YOLO v4 model with SSD, Faster RCNN, YOLO v3, and YOLO v5, it was found that the mAP of the improved YOLO v4 model was significantly higher than the other four models, which provided an efficient solution for intelligent diagnosis of pine wood nematode disease. The improved YOLO v4 model enables precise location and identification of pine wilt trees under changing light conditions. Deployment of the model on a UAV enables large-scale detection of pine wilt disease and helps to solve the challenges of rapid detection and prevention of pine wilt disease., Competing Interests: The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest., (Copyright © 2024 Zhang, Han, Wang, Li, Li, Zeng, Sun and Wu.)

In this paper, we are proposing a novel motion correction algorithm for high-resolution OR-PAM imaging. Our algorithm combines a modified demons-based tracking approach with a newly developed multi-scale vascular feature matching method to track motion between adjacent B-scan images without needing any reference object. We first applied this algorithm to correct motion artifacts within one three-dimensional (3D) data segment of rat iris obtained with OR-PAM imaging. We then extended the application of this algorithm to correct motions to obtain vasculature imaging in the whole mouse back. In here, we stitched five adjacent 3D data segments (large field-of-view) obtained while changing the focus of OR-PAM differently for each subarea. The results showed that the motion artifacts of both large blood vessels and microvessels could be accurately corrected in both cases. Compared to the manually stitching method and the traditional SIFT algorithm, the algorithm proposed in this paper has better performance in stitching adjacent data segments. The high accuracy of the motion correction algorithm makes it valuable in OR-PAM for high-resolution imaging of large animals and for quantitative functional imaging. [ABSTRACT FROM AUTHOR]

To improve the performance of 3D LiDAR imaging and reduce the negative effects of interference fringes on polarization modulation of electro-optical crystals, a calibration algorithm based on polarization modulation principle was proposed in this letter. The reflect light of targets is received by the system, modulated by an electro-optical crystal, then divided into two complementary light beams after passing through polarization beam splitting (PBS), and finally received by two electron-multiplying CCDs (EMCCDs). The depth map and intensity image can be reconstructed by adding the two modulated images. However, the cone interference effect of the electro-optic crystal affects the modulation of the polarized light, resulting in the distance error. By fitting the curve of grayscale ratio of each pixel acquired by two EMCCDs with the changing of voltage, the actual range can be calculated by the light intensity corresponding to the accurate polarization modulation. Ultimately, we found that the algorithm showed outstanding performance on high-precision imaging with an error less than 0.1m in a wide field-of-view of 0.9 mrad. [ABSTRACT FROM AUTHOR]

A nonlinear holographic technique is capable of processing optical information in the newly generated optical frequencies, enabling fascinating functions in laser display, security storage, and image recognition. One popular nonlinear hologram is based on a periodically poled lithium niobate (LN) crystal. However, due to the limitations of traditional fabrication techniques, the pixel size of the LN hologram is typically several micrometers, resulting in a limited field-of-voew (FOV) of several degrees. Here, we experimentally demonstrate an ultra-high-resolution LN hologram by using the laser poling technique. The minimal pixel size reaches 200 nm, and the FOV is extended above 120° in our experiments. The image distortions at large view angles are effectively suppressed through the Fourier transform. The FOV is further improved by combining multiple diffraction orders of SH fields. The ultimate FOV under our configuration is decided by a Fresnel transmission. Our results pave the way for expanding the applications of nonlinear holography to wide-view imaging and display.

The scanning fiber endoscope (SFE), an ultrasmall optical imaging device with a large field-of-view (FOV) for having a clear forward view into the interior of blood vessels, has great potential in the cardiovascular disease diagnosis and surgery assistance, which is one of the key applications for short-wave infrared biomedical imaging. The state-of-the-art SFE system uses a miniaturized refractive spherical lens doublet for beam projection. A metalens is a promising alternative that can be made much thinner and has fewer off-axis aberrations than its refractive counterpart. We demonstrate a transmissive metalens working at 1310 nm for a forward viewing endoscope to achieve a shorter device length and better resolution at large field angles. We optimize the metalens of the SFE system using Zemax, fabricate it using e-beam lithography, characterize its optical performances, and compare them with the simulations. The SFE system has a resolution of ∼ 140 μm at the center of field (imaging distance 15 mm), an FOV of ∼ 70 deg, and a depth-of-focus of ∼15 mm, which are comparable with a state-of-the-art refractive lens SFE. The use of the metalens reduces the length of the optical track from 1.2 to 0.86 mm. The resolution of our metalens-based SFE drops by less than a factor of 2 at the edge of the FOV, whereas the refractive lens counterpart has a ∼ 3 times resolution degradation. These results show the promise of integrating a metalens into an endoscope for device minimization and optical performance improvement. [ABSTRACT FROM AUTHOR]

Multiple lenses are used in most modern imaging systems to reduce deviations from the perfect optical imaging, which also results in a significant increase in prices. Computational Imaging Technology (CIT), which combines the traditional optical design and image reconstruction algorithms, has provided many methods for simplifying optical systems in recent years. However, the Field-of-View (FOV) and the relatively simple image degradation model limit the CIT approach. In this work, we present a novel and low-cost CIT approach for large-FOV imaging. Our system consists of a wide-angle optical module with two spherical lenses and a deep learning network for image reconstruction. Aiming at improving image quality, we introduce the Weighted Patch Degradation Model (WPDM) for the simple optical module with a wide range of spatial variants for large FOV and then construct a dataset. In addition, we present a DMPH-SE Network for our reconstruction task. Experiments show that our large-FOV imager could obtain excellent imaging results with a simple optical structure. • A design architecture that combines lens design and learned reconstruction for the large Field-of-View(FOV) is introduced. • An image degradation model for the large FOV is proposed. • A DMPH-SE network architecture based on deep learning was designed to deal with spatially variable degradation. [ABSTRACT FROM AUTHOR]

For high dynamic range (HDR) and large field-of-view (FOV) radio scenes, detection of faint sources with compact support and tiny sizes in comparatively extensive celestial areas is a challenging task. In the era of the Square Kilometer Array (SKA), it will be of particular concern, and data-targeted systematic methods are put on the agenda. This paper proposes a systematic method to solve this problem. From the perspective of image processing, a multiscale image contrast enhancement (ICE) is proposed to normalize the HDR. Source representation is realized by a starlet framework, where a two-step tone mapping (TM) including an arc-tangent function compression and an aggregated gain function enhancement are embedded. According to two bionic principles, the gain function is designed to highlight faint sources, remove artifacts (e.g., halos), and avoid feature distortions. For source detection, an integration method is proposed for projections between the celestial and the planar coordinate systems. It serves for coordinate transform and source location. A detection indicator system is built, and elaborate discrimination principles are drawn up. In the experiments, this method is dedicated to the dataset of the TIFR GMRT Sky Survey (TGSS) alternative data release 1 (ADR1) to facilitate the scientific goal of discovering new sources. Results show its advantages over the state of the art methods in visual inspections and detection ratios, and preliminarily prove that it has prospects for future HDR and large FOV radio data. [ABSTRACT FROM AUTHOR]

Laser-scanning imaging techniques are frequently used to probe the molecule spatial orientation in a sample of interest by exploiting selection rules depending on the polarisation of the excitation light. For the successful implementation of these techniques the precise control of the polarisation at the sample level is of fundamental importance. Polarisation distortions induced by the optical elements are often the main limitation factor for the maximum size of the field-of-view in polarisation-resolved (PR) laser-scanning microscopy, since for large scanning angles the polarisation distortions may mask the real sample structure. Here we shall demonstrate the implementation of large-field-of-view PR microscopy and show PR CARS imaging of mouse spinal cord thanks to a careful design of the laser-beam optical path. We shall show that this design leads to strongly suppressed distortions and quantify their effects on the final images. Although the focus of this work is on CARS imaging, we stress that the approaches described here can be successfully applied to a wide range of PR laser-scanning techniques. [ABSTRACT FROM AUTHOR]

In this paper, an all-dielectric frequency selective surface (ADFSS) was developed using genetic algorithms and fast Fourier transforms (FFTs) to generate random geometries. This device showed a stop-band fractional bandwidth (FBW) of 54% and a field of view of 16°. The optimized FSS was manufactured by three-dimensional (3-D) printing and the frequency response was measured in the laboratory. This device was also tested in the pass band at a high pulsed microwave power of \bf45.26\;\bfMW/\bfm^\bf2 and no damage was observed. This is the first known demonstration of a 3-D printed ADFSS. [ABSTRACT FROM PUBLISHER]

Abstract: Open-configuration magnetic resonance imaging (MRI) systems are becoming increasingly desirable for musculoskeletal imaging and image-guided radiotherapy because of their non-claustrophobic configuration. However, geometric image distortion in large fields-of-view (FOV) due to field inhomogeneity and gradient nonlinearity hinders the practical applications of open-type MRI. We demonstrated the use of geometric distortion correction for increasing FOV in open MRI. Geometric distortion was modeled and corrected as a global polynomial function. The appropriate polynomial order was identified as the minimum difference between the coordinates of control points in the distorted MR image space and those predicted by polynomial modeling. The sixth order polynomial function was found to give the optimal value for geometric distortion correction. The area of maximum distortion was<1 pixel with an FOV of 285mm. The correction performance error was increased at most 1.2% and 2.9% for FOVs of 340mm and~400mm compared with the FOV of 285mm. In particular, unresolved distortion was generated by local deformation near the gradient coil center. [Copyright &y& Elsevier]

To diagnose the implosion of a laser-driven-fusion target such as the symmetry, the hydrodynamic instability at the interface, a high-resolution, large field-of-view kilo-electron-volt X-ray imaging is required. A Kirkpatrick-Baez (K-B) microscope is commonly used, but its field of view is limited to a few hundred microns as the resolution decreases rapidly with the increase of the field of view. A higher resolution could be realized by using a Fresnel zone plate (FZP) for imaging. Presented in this work is a numerical study on the imaging properties of an FZP at Ti-Kα wavelength of 0.275 nm, and a comparison to a K-B imager. It is found that the FZP can realize not only a resolution better than 1 µm, but also a field-of-view larger than 20 mm when the FZP is illuminated by X-rays of spectral bandwidth less than 1.75%. These results indicate the feasibility of applying the FZP in high-resolution, large field-of-view X-ray imaging. [ABSTRACT FROM PUBLISHER]

• A novel calibration method based on a 3D flexible control field is proposed. • This method requires a narrow calibration space and adapts to the varied FOV. • It is simple to operate and the workload remains unchanged with FOV changing. Binocular stereo vision sensor (BSVS) based on disparity is an important technique in 3D shape measurement. In the application of large field-of-view (FOV), a relative short baseline distance of BSVS has heightened the demand for calibration. In this paper, a calibration method based on a virtual 3D flexible control field (FCF) for BSVS with short-baseline is proposed. The FCF is constructed by a laser tracker, a precision two-axis turntable and a camera calibration reference target (CRT) which can be exchanged with a standard laser tracker target precisely. Firstly, the turntable coordinate system is built and the position of the fixed CRT is measured by the laser tracker. Then the turntable with BSVS on it rotates in two dimensions regularly, during which the cameras shoot the fixed CRT synchronously, and the 3D FCF is constructed. Finally, the minimum objective function is established according to the BSVS imaging model to realize the calibration of internal and external parameters integrally. The experimental results show that under the conditions that the baseline is 120 mm and the working distance is 3 m ∼ 5 m , the residual error RMS of spatial points is 0.3654 mm and the reconstructed distance RMS error is 1.0728 mm. [ABSTRACT FROM AUTHOR]

Laser-scanning imaging techniques are frequently used to probe the molecule spatial orientation in a sample of interest by exploiting selection rules depending on the polarisation of the excitation light. For the successful implementation of these techniques the precise control of the polarisation at the sample level is of fundamental importance. Polarisation distortions induced by the optical elements are often the main limitation factor for the maximum size of the field-of-view in polarisation-resolved (PR) laser-scanning microscopy, since for large scanning angles the polarisation distortions may mask the real sample structure. Here we shall demonstrate the implementation of large-field-of-view PR microscopy and show PR CARS imaging of mouse spinal cord thanks to a careful design of the laser-beam optical path. We shall show that this design leads to strongly suppressed distortions and quantify their effects on the final images. Although the focus of this work is on CARS imaging, we stress that the approaches described here can be successfully applied to a wide range of PR laser-scanning techniques., (© 2015 The Authors. Journal of Microscopy published by John Wiley & Sons Ltd on behalf of Royal Microscopical Society.)