Obtaining real-time laser atmospheric transmittance is crucial for laser technology applications in many scientific fields. Based on measurements and research on simulations, a method using the sun-photometer direct radiation measurements to extract infrared laser atmospheric transmittance is proposed herein. The method is effective, inexpensive, and can be used simultaneously for multiple laser wavebands. Compared with the measurements obtained using a POM02-type sun-photometer, errors of transmittance and precipitable water measured using a homemade ISP-type near-infrared sun-photometer are less than 7%. The 1.315-μm transmittance is extracted from 1.31-μm and 1.32-μm data. The extraction errors proportional to the precipitable water are less than 4% for two sun-photometers. Compared with precipitable water reversed from 0.94-μm data, the extraction error of precipitable water reversed from 1.32-μm data is less than 10%. Therefore, the precipitable water can be reversed from 1.32-μm data when 0.94-μm data is not available. The statistical error of 1.315-μm laser atmospheric transmittance calculated by the proposed method is less than 6% compared with the results calculated using laser transmission evaluation software based on real-time measurements of atmospheric parameters. This method is valuable for laser engineering applications in the real atmosphere.

To compare the propagation characteristics of super Gaussian vortex beam with Gaussian vortex beam in air, the transmission equation of these beams in a turbulent atmosphere are deduced by using the Fresnel diffraction integral formula. Using the random phase screen method, an atmospheric turbulence analysis model is established, and intensity distributions and beam quality are calculated for vortex beams with different parameters. Results indicate that beam quality is influenced by topological charge, transmission distance, and turbulence intensity. Among these factors, transmission distance has the most obvious effect on beam quality for a super Gaussian vortex beam, whereas topological charge has a more obvious effect on Gaussian vortex beam quality.

In this study, the high-resolution Fourier transform infrared spectroscopy (FTIR) is used to detect the concentrations of nitric acid (HNO3) in the atmosphere above the Hefei site. The vertical profiles and total columns of HNO3 are retrieved from the mid-infrared solar absorption spectra using the optimal estimation method. The vertical profiles and time series of the total columns of atmospheric HNO3 are obtained over the entire year of 2017. Further, the characteristics of the seasonal variation of HNO3, sensitivity altitude of concentration detection, averaging kernels of retrieved profiles, and degrees of freedom are analyzed. The vertical profiles of atmospheric HNO3 in different seasons denote that the HNO3 concentrations are higher at an altitude of 20--30 km in the stratosphere and that they are lower in the troposphere. Furthermore, the total columns of HNO3 exhibit obvious seasonal variations, with a maximum in spring and minimum in winter. The amplitude of the seasonal variations is 9.82×10 15 molecule/cm 2. The data products obtained from the Aura MLS satellite are selected for performing comparison with the ground-based data to validate the measurements of the ground-based FTIR using independent data. The comparison results denote that the ground-based remote sensing and satellite observations display a consistent seasonal HNO3 variability. The ground-based data exhibits a good agreement with the satellite data with a high correlation coefficient of 0.83 even though the partial columns of the satellite data are lower than the corresponding ground-based total columns. The observation results indicate the reliability and accuracy of the ground-based FTIR for observing the temporal and spatial distributions of the atmospheric HNO3.

Ultraviolet light guided flight offers many advantages for unmanned aerial vehicles (UAVs), such as strong anti-interference ability, all-weather operation, non-line-of-sight communication, strong secure communication ability, and suitability to special situations. This study presents a wireless ultraviolet guided method that assists UAV matching of different terrain flights, and proposes a control method of optical power based on ultraviolet light. By adjusting the angle and power of the transmitter of the ultraviolet beacon, the power of the ultraviolet light reaching the flight plane of the UAV is equalized, thereby ensuring the safety of the UAV flight. The power control method is simulated on a computer. When the transmission angle of the ultraviolet LED is 80° or less, the power is easily controlled; moreover, the beacon integration is easily achieved and is less affected by the terrain and height, among other factors.

In this study, we propose an ultrasensitive refractive index sensor based on tapered optical fiber couplers and assisted with the vernier effect to overcome the problem of low sensitivity of the conventional optical fiber refractive index sensors. Two passes of mode interferences between the even and odd modes, can be achieved in both x-polarization and y-polarization by utilizing the birefringence effect of the tapered optical fiber couplers, and the superposition of these two interferences results in the vernier effect. Based on theoretical analysis and numerical calculations, the difference in the group birefringence coefficient between the even and odd modes is observed to denote zero points when the width of the tapered optical fiber coupler is 1.6--3.2 μm, significantly enhancing the sensitivity of the refractive index sensor. Further, we experimentally demonstrate our theoretical results using a tapered optical fiber coupler with a width of 1.6 μm, achieving ultrahigh sensitivities of 30020.0 nm/RIU and -34402.5 nm/RIU near the refractive index of 1.333. Our sensor is easy to fabricate, compact in structure, and cost effective, and it exhibits significant application prospects in the fields of ultrasensitive biomedical detection and analytical chemistry.

Traditional infrared and visible image fusion method decomposes images into several frequency components, fuses them separately, and then adds them together, resulting in problems of edge fuzziness, low contrast, and so on. The paper proposes a fusion method based on Tikhonov regularization and detail reconstruction. Firstly, images are decomposed into base layers and detail layers by Tikhonov regularization. A generative adversarial network is trained aiming at detail information reconstruction for base layers. Secondly, features of base layers to be fused are extracted, and the principal component analysis method is used for feature fusion. Finally, the fused results of base layers are input into generative network to reconstruct a fusion image with abundant high frequency information. Experimental results show that the method proposed in this paper preserves detail information and highlight areas of the source images well, with a good robustness to the images with different resolutions.

A retinal vessel segmentation method based on the affinity propagation clustering of superpixels was proposed herein. First, the maximum Hessian eigenvalue, the Gabor wavelet, and the B-COSFIRE filtering features were extracted from the preprocessed image to construct the three-dimensional fundus image. The fundus image was segmented into superpixel blocks, which were screened based on a pixel consistency criterion to select the best candidates; these candidates were considered as sample points and their statistical average pixel values were used as the feature vectors. Two clustering centers of the vessel and background classes were obtained by performing affinity propagation clustering on the feature space. Based on these clustering centers, the fundus pixels were classified via the nearest neighbor method for retinal vessel segmentation. The experimental results show that the accuracies are 94.63% and 94.30% for the DRIVE and STARE fundus image databases, respectively. Compared with K-means clustering, FCM (Fuzzy C-means), and other clustering methods, the proposed technique presents a high recognition degree for blood vessels and better continuity and integrity of the segmented retinal vessels.

The traditional single image dehazing algorithms are susceptible to the prior information of hazy images, resulting in color distortion. Furthermore, the deep-learning dehazing algorithms are limited by the network model, leading to residual haze. To overcome these problems, this study proposes a single image dehazing method of multiscale deep-learning based on dual-domain decomposition. This method develops a multiscale deep-learning network model that includes low- and high-frequency dehazing subnets. Firstly, the hazy image is decomposed using bilateral filters to obtain high- and low-frequency sub-images of the hazy image. Subsequently, the mapping relations between the high- and low-frequency sub-images as well as the high- and low-frequency transmissivity of the hazy image are learned using the developed network model. The high- and low-frequency transmissivity obtained by model learning is fused to obtain the scene transmissivity of the original hazy image. Finally, the hazy image is restored to the dehazed image based on the atmospheric scattering model, which is trained and tested using the hazy image dataset. The experimental results denote that the proposed method can achieve a good dehazing effect for the synthetic hazy images and real natural hazy images and that it is superior to other contrast algorithms in subjective and objective evaluations.

To resolve the issues of poor consistency, large volume, and difficult integration for a multi-alternating light source, a precise linear displacement measurement method for micro-controlled phase shift is proposed based on the electric traveling wave synthetized from the standing wave of single-alternating light field, herein. The proposed method uses a single-alternating light source and the spatial modulation of a four-channel sinusoidal grid to obtain four-channel light intensity signals, resulting in two-channel standing wave signals with a strict phase difference of 90° by the micro-controlled phase-shifting circuit. A one-channel traveling wave signal is synthesized from the two-channel standing wave signals. Finally, the phase difference between the reference signal and the electric traveling wave signal is interpolated by a high-frequency clock pulse to realize a displacement measurement. In addition, the principle of micro-controlled phase-shifting measurement is analyzed, the theoretical and simulation models of phase-shifting accuracy and light-field distribution are established, and the causes of primary and secondary errors introduced by phase-shifting accuracy and light-field distribution of the sensor are clarified. Experimental results demonstrate an original measurement accuracy of ±0.26 μm for the prototype in a short period of 0.6 mm; after applying error correction and optimization, the resultant measurement accuracy is basically similar to that of the RENISHAW laser interferometer over a measurement range of 500 mm. This integrated structure and high-precision measurement results provide a solid foundation for future engineering applications of these sensors.

The correlated color temperature (CCT) is an important parameter to characterize light sources. However, no analytical expression has been provided by international organizations for evaluating the uncertainty of CCT. The uncertainty propagation law and Monte Carlo method are used to analyze the uncertainty of CCT. When using the uncertainty propagation law method, a CCT sensitivity coefficient formula can be derived based on the definition of CCT to evaluate its uncertainty. The variation in CCT due to the change in spectral power evaluated via numerical calculations is used to validate the formula. When using the Monte Carlo method, a group of spectral data can be generalized using numerical simulations. The corresponding CCT and its distribution are calculated to obtain the uncertainty of CCT. The results obtained using the uncertainty propagation law and Monte Carlo method exhibit a good agreement. When the spectral correlation is neglected, the uncertainty of CCT is observed to be proportional to the square root of the wavelength interval.

A method of introducing a binary optical lens into spectral confocal micro-spacing measurement is proposed to address the problems that the dispersion linearity of a conventional dispersion lens is difficult to ensure absolute linearity and that the measurement range is narrow, meanwhile minimizing spherical aberration, particularly the axial spherical aberration which has the greatest influence on measurement. In the proposed method, the dispersion of the binary optical lens is only related to the wavelength of the incident light and it is strictly inversely proportional to the wavelength of the incident light. Binary optical lenses do not exhibit spherical aberration problems and can also compensate for the unknown chromatic aberrations of other optical components in the calibration measurement system. In the experiment, a spectrum in the range of 510--690 nm is selected, and the spectral information is received by a spectrometer with a 0.5-nm resolution. Furthermore, the measurement range of this method is 13.95 mm, the measurement error is 0.6 μm, and the recording spacing with respect to the optical disc is measured to verify the effectiveness of the proposed method.

To solve the problems of inherent errors caused by the residual stray light after the stray light limitation of the optical systems as well as time-varying errors caused by system aging and improve the accuracy of the optical measurement system, an error correction algorithm is proposed for a photometric measurement system based on the radial basis function (RBF) network. In this study, the point source transmittivity is used to analyze the measurement error distribution of the photometric measurement system. Further, the error correction algorithm is designed and improved based on the RBF network in accordance with the approximate estimation of stray light distribution. TracePro software is employed for comparative simulations. The results demonstrate that the photometric measurement error can be reduced to less than 0.24% by correcting the error compensation network. Compared with the general RBF network, the convergence speed and approximation ability of the proposed algorithm are obviously improved, providing a more rapid and effective tool for solving the problems entailed by the stray light limitation.

In this study, a high-efficiency fiber gas laser source is developed in a single-pass deuterium-filled hollow-core photonic crystal fiber by rotational stimulated Raman scattering (SRS). The ordinary dominant vibrational SRS with relatively large gain is suppressed because of the special transmission properties of this low-loss hollow-core photonic crystal fiber, permitting its efficient conversion to a rotational Stokes wave. A homemade 1540-nm nanosecond pulsed fiber amplifier is used to pump a hollow-core photonic crystal fiber having a length of 20 m with a high-pressure deuterium gas. Thus, a high-efficiency 1645-nm Raman laser is obtained using a single-pass configuration. A maximum average output power of ~0.8 W (single-pulse energy of ~1.6 μJ) can be obtained with respect to the 1645-nm Raman laser at a pressure of 2 MPa, and the slope efficiency is observed to be ~71.4%. This study introduces a simple and effective way for the realization of 1.7-μm-band fiber lasers.

In this study, a method is proposed based on multiscale local and deep features to address the difficulty associated with finding exactly matching pixels from the ill-posed regions in stereo matching. The feature fusion stage comprises two parts. First, the shallow features with different scales, including the Log-Gabor features and the local binary pattern features, are fused. The second part integrates the multiscale shallow fused features and deep features via a convolutional neural network and forms the final feature image, which contains both the semantic and structural information. Further, a positive and negative sample construction method is proposed by adding some noise in the vertical direction to reduce the error that can be attributed to imprecise epipolar alignment in an image. The proposed method is compared with two variant methods (changing or discarding of some modules) with respect to the KITTI datasets. The experimental results validate the effectiveness of the module settings with respect to the proposed method. This method also achieves competitive matching results when compared with those achieved using some classical methods.

Objectdetection technology based on deep learning has shown excellent performance in the field of object detection; however, it has not yielded expected results when used for synthetic aperture radar (SAR) ship detection. Herein, an SAR ship detection method based on a convolutional neural network is proposed for multiscale ship detection in multiple scenarios. Based on the single shot multiBox detector, we use Darknet-53 as the feature extraction network. A deep feature fusion network is added to generate new feature prediction maps with rich semantic information. In addition, we use a new two-class loss function in the training strategy to deal with the imbalance in the difficult and easy samples in the training process. The verification experiments are performed on the expanded public SAR ship detection dataset. The experimental results indicate that our proposed method has a good adaptability to SAR ship detection at different sizes in complex scenes.

Subsurface defects directly affect the imaging quality and laser damage threshold of fused silica optical components. In comparison with the two-dimensional cross-section size and depth of the defect, the quantitative detection results of the three-dimensional contour of the subsurface defect and its volume can facilitate a more accurate evaluation of the processing quality of the fused silica optical component. In this work, a tomography scanning experiment of the fused silica sample is performed using a confocal microscope based on the principle of confocal microscope. Moreover, a three-dimensional reconstruction algorithm for the subsurface defects of fused silica components is proposed based on the analysis of the characteristics of subsurface defect images. The proposed algorithm outperforms other reconstruction methods in terms of efficiency and accuracy. Based on the statistical results of the reconstructed defects, the complete three-dimensional information of the subsurface defects of the fused silica samples is quantitatively obtained.

Based on the higher-order nonlinear Schr?dinger equation describing ultrashort pulse transmission in metamaterials, this study presents an exact femtosecond quasi-bright soliton solution and determines its existence conditions by using the traveling wave method. When the group speed dispersion, third-order dispersion, cubic-quintic nonlinearities, self-steepening, and second-order nonlinear dispersion effects are properly balanced, the femtosecond quasi-soliton can exist in nonlinear metamaterials. Without the third-order dispersion and second-order nonlinear dispersion, the soliton in metamaterials can not occur. Based on the Drude model, the existence index regions of the femtosecond quasi-bright soliton are discussed in different nonlinear metamaterials. The results show that femtosecond quasi-soliton can exist in the negative index region of self-defocusing nonlinear metamaterials, and in the positive index region of self-focusing nonlinear metamaterials. Moreover, the intensities and widths of the solitons differ in different regions of the metamaterials, implying that the properties of the formed solitons can be adjusted by choosing different nonlinear metamaterials and different frequencies of the incident wave,making them in the corresponding existence areas.

The study proposes a 30× continuous zoom lens for 640×512 cooled mid-wave infrared focal plane detector array. Furthermore, we propose an optical system design that separates the lens from the front fixed group as a two-speed moving group, and combines the two-speed moving group, variable magnification group, compensation group, and rear fixed group into a composite continuous zoom system. To realize the above model, we design a cooling-type medium-wave infrared continuous zoom optical system based on the theory of diffractive optics. It has a working band of 3.7--4.8 μm, F-number of 4, continuously variable focal length of 12--360 mm, and total optical length of only 160 mm. We optimize the image quality and cam curves of the system at 6 focal lengths. The system offers advantages of a large variable ratio, miniaturized structure, excellent image quality,and smooth cam curve, which meets the requirements of infrared thermal imagers.

In this work, a top-cut hexagonal prism is designed. The six sides of the prism are divided into two equal groups, in which the angles between the side and the bottom of the prism are different. When an expanded plane wave is refracted by the six sides of the prism, the interference pattern can be formed, which is corresponding to a two-period superlattice structure of gradient photonic crystal (GPC). If the interfered pattern is recorded by a photosensitive medium, a GPC lens array is obtained. By adjusting the angle between the side and bottom of the prism and the amplitude contrast of both groups of beams, we can design lenses and arrays with different array periods, gradients, and refractive index differences between the center and edge of each lens. This work provides new applications for gradient lenses in optical interconnection, optical integration, optical coupling, display, and imaging.

This paper investigates the effects of three weather conditions, such as rain, snow, and haze, on security key rate of a free-space quantum communication system using different signal wavelengths. For the cases of collective and individual attacks, analysis is conducted on why and how different weather conditions and signal wavelengths affect the security key rate based on a continuous-variable quantum key distribution model with Gaussian modulation. In the analysis, two different conditions are considered: one in which Bob uses a homodyne detector and the other in which a heterodyne detector is used. The relationship between the security key rate and transmission distance is simulated under various conditions, including different weather conditions and levels, different quantum signal wavelengths, different attack types, and different detection modes. The results of this paper can serve as an instructive reference for future practical free-space quantum communication system designs.

Traditional feature extraction algorithms consider only spectral information in the hyperspectral image (HSI) and cannot extract fine spatial information. To solve this problem, this paper proposes a supervised spatially-regularized manifold discriminant analysis (SSRMDA) algorithm to improve the classification performance of ground objects in the HSI. The SSRMDA algorithm firstly constructs a spectral-domain intraclass image and an interclass image by using the label information of training samples, which reveals the potential nonlinear manifold structure of hyperspectral data. Based on that, a spatial-domain intraclass image is constructed, and it combines the spectral information of HSI by regularization to realize the effective fusion of spectral-spatial information. In low-dimensional space, the intraclass data in low dimensional space becomes more clustered and the separability of embedded features is enhanced. Experiments on the Indian Pines and Washington DC Mall datasets show that the overall classification accuracy of the SSRMDA algorithm reaches 91.58% and 96.67%, respectively, which denotes that the proposed algorithm effectively improves the classification ability of ground objects. Compared with other feature extraction algorithms, the proposed algorithm is effective in practical applications, especially when a small number of training samples are available.

In this paper, using high-precision timers, photoelectric detectors, and other equipment, the laser transmitting and receiving system delays for a 60-cm receiving telescope aperture system at the Shanghai Observatory are measured and calibrated, with a transmitting telescope aperture of 21 cm. By comparing the result of a target measurement by a space debris laser ranging (SDLR) system on the ground with the measured results of the conventional target measurement, we find that the calibration error of delay is approximately 400 ps. On this basis, using a high-precision clock system in a laser ranging system, SDLR is achieved for the first time in China using a single telescope to send laser pulses and double telescopes with a distance of 2.5 km to receive laser echoes, solving the problem of detection range gate control of echo signals from the remote telescope. This system realizes a range distance of over 1000 km. The proposed method demonstrates the ability to receive laser echoes from space debris from a remote distance on the ground with multiple telescopes.

A miniature snapshot Fourier-transform imaging spectrometer is proposed to realize the real-time detection of images and spectra of moving scenes and fast-changing targets. Interferometric imaging spectra are acquired in real time using two multi-stage micro-mirrors to modulate the distributed phase of multiple image fields formed by a micro-lens array. Based on phase modulation by the micro-lens array and multi-stage micro-mirrors on the optical field, a theoretical model is established for multiple interferometric imaging. Numerical results show that the interferometric image points with different field of view (FOV) are located at different areas on the detector plane. However, in the case of a large FOV, the interferometric image units between adjacent channels have crosstalk and the recovered spectrum is distorted. To suppress the crosstalk between adjacent channels, the FOV angle can be controlled within the limited FOV determined by the diffraction and defocusing effects of the micro-lens array and multi-stage micro-mirrors. Simultaneously, the FOV introduces phase errors, which causes monotonously increasing normalized spectral errors according to the FOV angle. Based on the normalized spectral error analysis results, the FOV can be rationally designed to achieve effective detection of a specific area in target scenes.

Plasma mirrors can be effectively used to improve the signal-to-noise ratio (SNR) of high-power ultrashort laser; however, some experiments conducted using plasma mirrors denote that plasma mirrors may cause focal spot deterioration. Herein, we establish a spatiotemporal focusing multistep propagation algorithm based on plasma expansion and diffraction propagation to quantitatively investigate the plasma-mirror-induced focal spot deterioration. Further, the influences of the plasma expansion time as well as the amplitude and spatial frequency of wavefront error on focal spot deterioration are quantitatively analyzed using a beam quality evaluation function. The simulation results reveal that the plasma-mirror-induced far-field focal spot deterioration can be mainly attributed to the non-uniform plasma expansion based on the plasma expansion time and wavefront error, with the plasma expansion time observed to be the dominant factor. Additionally, higher-amplitude and lower-spatial-frequency wavefront errors have a relatively greater influence on the focusing ability. From the perspective of high SNR ultraintense output capability, the spatiotemporal quality requirement of the pulse is introduced to avoid far-field focal spot deterioration when a plasma mirror is used in a high-power ultrashort laser system.