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In order to solve the star identification problem in the lost space mode for scientific cameras with small fields of view and higher instruction magnitudes, this paper proposes a star identification algorithm based on a hybrid grid pattern. The application of a hybrid pattern generated by multi-calibration stars in the initial matching enables the position distribution features of neighboring stars around the main star to be more comprehensively described and avoids the interference of position noise and magnitude noise as much as possible. Moreover, calibration star filtering is adopted to eliminate incorrect candidates and pick the true matched navigation star from candidate stars in the initial match. Then, the reference star image is utilized to efficiently verify and determine the final identification results of the algorithm via the nearest principle. The performance of the proposed algorithm in simulation experiments shows that, when the position noise is 2 pixels, the identification rate of the algorithm is 96.43%, which is higher than that of the optimized grid algorithm by 2.21% and the grid algorithm by 4.05%; when the magnitude noise is 0.3 mag, the star identification rate of the algorithm is 96.45%, which is superior to the optimized grid algorithm by 2.03% and to the grid algorithm by 3.82%. In addition, in the actual star image test, star magnitude values of ≤12 mag can be successfully identified using the proposed algorithm. [ABSTRACT FROM AUTHOR]

Medical image segmentation remains particularly challenging for complex and low‐contrast anatomical structures, especially in brain MRI glioma segmentation. Gliomas appear with extensive heterogeneity in appearance and location on brain MR images, making robust tumour segmentation extremely challenging and leads to highly variable even in manual segmentation. U‐Net has become the de facto standard in medical image segmentation tasks with great success. Previous researches have proposed various U‐Net‐based 2D Convolutional Neural Networks (2D‐CNN) and their 3D variants, called 3D‐CNN‐based architectures, for capturing contextual information. However, U‐Net often has limitations in explicitly modelling long‐term dependencies due to the inherent locality of convolution operations. Inspired by the recent success of natural language processing transformers in long‐range sequence learning, a multi‐view 2D U‐Nets with transformer (TransMVU) method is proposed, which combines the advantages of transformer and 2D U‐Net. On the one hand, the transformer encodes the tokenized image patches in the CNN feature map into an input sequence for extracting global context for global feature modelling. On the other hand, multi‐view 2D U‐Nets can provide accurate segmentation with fewer parameters than 3D networks. Experimental results on the BraTS20 dataset demonstrate that our model outperforms state‐of‐the‐art 2D models and classic 3D model. [ABSTRACT FROM AUTHOR]

The object detection technologies of remote sensing are widely used in various fields, such as environmental monitoring, geological disaster investigation, urban planning, and military defense. However, the detection algorithms lack the robustness to detect tiny objects against complex backgrounds. In this paper, we propose a Multiple Attention Mechanism Enhanced YOLOX (MAME-YOLOX) algorithm to address the above problem. Firstly, the CBAM attention mechanism is introduced into the backbone of the YOLOX, so that the detection network can focus on the saliency information. Secondly, to identify the high-level semantic information and enhance the perception of local geometric feature information, the Swin Transformer is integrated into the YOLOX's neck module. Finally, instead of GIOU loss, CIoU loss is adopted to measure the bounding box regression loss, which can prevent the GIoU from degenerating into IoU. The experimental results of three publicly available remote sensing datasets, namely, AIBD, HRRSD, and DIOR, show that the algorithm proposed possesses better performance, both in relation to quantitative and qualitative aspects. [ABSTRACT FROM AUTHOR]

To detect faint target stars of 22nd magnitude and above, an astronomical exploration project requires its space camera's readout noise to be less than 5e− with long-time working stability. Due to the limitation of satellite, the traditional CCD detector-based camera does not meet the requirements, including volume, weight, and power consumption. Thereby, a low-noise ultra-stable camera based on 9 K × 9 K large target surface CMOS is designed to meet the needs. For the first time, the low-noise ultra-stable camera based on CMOS detector will be applied to space astronomy projects, remote sensing imaging, resource survey, atmospheric and oceanic observation and other fields. In this paper, the design of the camera is introduced in detail, and the camera is tested for several rounds at −40 °C; it also undergoes further testing and data analysis. Tests proved super stability and that the readout noise is lower than 4.5e−. Dark current, nonlinearity and PTC indicators meet the requirements of the astronomical exploration project. [ABSTRACT FROM AUTHOR]

Sheared-beam imaging (SBI) is an effective way of imaging through turbulent medium, such as atmosphere or scattering liquid. Traditionally, the imaging is based on the laser transmitter array consisting of three beams or five beams for coherent illumination to the remote object. Compared with the existing SBI methods, the four-beam sparse sampling imaging method has been proposed, which may have more advantages; it not only sparses the detector elements but also reduces the number of emitted beams. However, the traditional phase retrieval algorithms are not suitable for the four-beam sparse sampling imaging. We propose a four-beam sparse phase retrieval (F-BSPR) algorithm, which uses the phase differences from both horizontal and vertical components and the phase differences from other components when the phase is retrieving. The proposed phase retrieval algorithm can better connect the phase difference and improve the accuracy of the phase retrieval. Furthermore, the imaging quality is improved. Simulation and experimental results show that the proposed algorithm is effective and feasible when the number of detector elements is sparse by 50%. Compared to the traditional four-beam phase retrieval method, the proposed F-BSPR method has better imaging quality and robustness. [ABSTRACT FROM AUTHOR]

3D feature descriptors provide essential information to find given models in captured scenes. In practical applications, these scenes often contain clutter. This imposes severe challenges on the 3D object recognition leading to feature mismatches between scenes and models. As such errors are not fully addressed by the existing methods, 3D feature matching still remains a largely unsolved problem. We therefore propose our Histograms of Gaussian Normal Distribution (HGND) for capturing salient feature information on a local reference frame (LRF) that enables us to solve this problem. We define a LRF on each local surface patch by using the eigenvectors of the scatter matrix. Different from the traditional local LRF-based methods, our HGND descriptor is based on the combination of geometrical and spatial information without calculating the distribution of every point and its geometrical information in a local domain. This makes it both simple and efficient. We encode the HGND descriptors in a histogram by the geometrical projected distribution of the normal vectors. These vectors are based on the spatial distribution of the points. We use three public benchmarks, the Bologna, the UWA and the Ca' Foscari Venezia dataset, to evaluate the speed, robustness, and descriptiveness of our approach. Our experiments demonstrate that the HGND is fast and obtains a more reliable matching rate than state-of-the-art approaches in cluttered situations. [ABSTRACT FROM AUTHOR]

Abstract The vehicle theodolite has been the trend of the optical shooting range survey. While tracking and imaging of the long-distance dim targets have not been solved perfectly in this field, so this paper presents one active polarization imaging technology to find a way to deal with it. Firstly, we put forward a new active polarization imaging system with the lighting mode of circularly polarized laser and receives the reflected light by the mode of range-gating technique; secondly, deduces the pulse laser beam pointing algorithm; thirdly, describes the extended Kalman filtering algorithm to compensate the delay in the process and the calculation of the polarization angle, the attitude angle of flight target using single station cosine method and the depolarization of the circularly polarized laser. Finally, the result of this active polarization imaging technology which is simulated and analyzed in Simulink shows that the pointing precision can reach 0.0075 degrees. Then the analysis in this paper provide a theoretical basis for the application of the active polarization imaging technology in the field of the optical shooting range survey. [ABSTRACT FROM AUTHOR]

Highlights • We formulate a new LRF with the projection by the scatter matrixs Eigen vectors and the mesh normal. • We firstly discuss the support radius for constructing the LRF and generating the feature descriptor separately. • In the experiments about Recall vs. 1-Precision Curve, we provide more precise measurements for TP (true positive) and FP (false positive). • Our methods concentrate on decreasing the calculation time and storage through all the processes. (e.g. low feature generation time and low feature dimension). Graphical abstract Abstract Object recognizing in cluttered scenes remains a largely unsolved problem, especially when applying feature matching to cluttered scenes there are many feature mismatches between the scenes and models. We propose our Feature Fusion Information Statistics (FFIS) as the calculation framework for extracting salient information from a Local Surface Patch (LSP) by a Local Reference Frame (LRF). Our LRF is defined on each LSP by projecting the scatter matrix's eigenvectors to a plane which is perpendicular to the normal of the LSP. Based on this, our FFIS descriptor of each LSP is calculated, for which we use the combined distribution of mesh and point information in a local domain. Finally, we evaluate the speed, robustness and descriptiveness of our FFIS with the state-of-the-art methods on several public benchmarks. Our experiments show that our FFIS is fast and obtains a more reliable matching rate than other approaches in cluttered situations. [ABSTRACT FROM AUTHOR]

Hypothesis Generation (HG) and Hypothesis Verification (HV) play an important role in 3D objection recognition. However, performing 3D object recognition in cluttered scenes using HG and HV still remains a largely unsolved problem. High False Positive (FP) in HG and HV stages are witnessed due to clutter and occlusion, which will further affect the final accuracy of recognition. To address these problems, we propose a novel Hough-space-based HG approach for extracting hypotheses. Differently from the existing methods, our approach is based on a Hough space which adopts a self-adapted measure to generate hypotheses. Based on this, a novel HV-based method is proposed to verify the hypotheses obtained from HG procedures. The proposed method is evaluated on four public benchmark datasets to verify its performance. Experiments show that our approach outperforms state-of-the-art methods, and obtains a higher recognition rate without sacrificing precision both at high FP rates and high occlusion rates. [ABSTRACT FROM AUTHOR]

Aim to the problem of initial epipolar distance transformation algorithm is sensitive to illumination and depth discontinuous, an improved stereo matching algorithm is proposed. In Delta-Gama log-chromaticity space, Gamma correction coefficient is computed firstly according to raw disparity map which is used to Modified the similarity function bandwidth. Secondly, by employing image structure and color features, the segment of epipolar is obtained adaptively. At last, final disparity map is acquired by belief propagation method. Experimental results demonstrate the effectiveness of the proposed transform in illumination change regions and discontinuities regions. The matching rate of proposed algorithm is improved about 60%, comparing with the initial epipolar distance transformation algorithm and state-of-art algorithm such as ANCC(Adaptive Normalized Cross Correlation). [ABSTRACT FROM AUTHOR]

In the field of the active wavefront correction for off-axis telescopes, the sensitivity matrix and damped least squares method are widely employed to calculate the misalignment. Improper selection of the damping coefficient will lead to bad wavefront correction results. Moreover, the calculated misalignment is referenced on the optical coordinate system, which cannot be directly applied as the control quantity. The article has two innovative points to solve these problems. First, an adaptive damping least squares method is proposed. The method considers the mirror surface error, uses Python + Zemax cosimulation to perform closed-loop reverse verification, and selects the optimal damping coefficient. Simulation is carried out for verification. Second, the article deduces the mathematical relationship between the calculated misalignment and the mechanism control quantity. Based on the above research, the wavefront active correction experiment has been completed. The optical component is actively adjusted with the wavefront quickly converging to RMS = 0. 055λ @ 632. 8 nm. The results verify the correctness of the proposed method. [ABSTRACT FROM AUTHOR]

This study explored the optical spectrum evolution process using a pump-modulated light and a continuous-wave probe, launched simultaneously into a 1 km highly nonlinear fiber. A total of 70 optical spectra were obtained by each changing the wavelength spacing (0.4 nm) between the probe and pump lights. Simulation results indicated that wavelength spacing between the two beams caused a cyclical optical spectrum evolution process induced by cross-phase modulation. As input light wavelength spacing increased, the coupling between the two optical fields showed obvious attenuation in each neat, multi-peak cycle. [ABSTRACT FROM AUTHOR]

Abstract: Based on the improved vector quantization (IVQ) algorithm [1] which was proposed in 2012, this paper proposes a further improved vector quantization (FIVQ) algorithm for LASIS (Large Aperture Static Imaging Spectrometer) interference hyper-spectral image compression. To get better image quality, IVQ algorithm takes both the mean values and the VQ indices as the encoding rules. Although IVQ algorithm can improve both the bit rate and the image quality, it still can be further improved in order to get much lower bit rate for the LASIS interference pattern with the special optical characteristics based on the pushing and sweeping in LASIS imaging principle. In the proposed algorithm FIVQ, the neighborhood of the encoding blocks of the interference pattern image, which are using the mean value rules, will be checked whether they have the same mean value as the current processing block. Experiments show the proposed algorithm FIVQ can get lower bit rate compared to that of the IVQ algorithm for the LASIS interference hyper-spectral sequences. [Copyright &y& Elsevier]

Abstract: The effect of competing nonlinearity on beam dynamics in parity-time () symmetric potentials is investigated. By using numerical methods, the existence of gap solitons is demonstrated in the first Bragg band gap of optical () symmetric lattices with competing nonlinearity. Meanwhile, the stability of such solitons is analyzed through introducing a small perturbation to the solitary solutions. The abrupt annihilation of the solitons during propagation demonstrates that the Bragg gap solitons in symmetric potentials are not stable. In comparison with the on-site gap solitons, the off-site gap solitons exhibit more robust properties during propagation. [Copyright &y& Elsevier]

Abstract: According to the imaging principle and characteristic of LASIS (Large Aperture Static Interference Imaging Spectrometer), we discovered that the 3D (three dimensional) image sequences formed by different interference pattern frames, which were formed in the imaging process of LASIS Interference hyperspectral image, had much stronger correlation than the original interference hyperspectral image sequences, either in 2D (two dimensional) spatial domain or in the spectral domain. We put this characteristic into image compression and proposed an adaptive OPD (optical path difference) and dislocation prediction algorithm for interference hyperspectral image compression. Compared the new algorithm proposed in this paper with Dual-Direction Prediction [1] proposed in 2009, lots of experimental results showed that the prediction error entropy of the new algorithm was much smaller. In the prediction step of lifting wavelet transform, this characteristic would also reduce the entropy of coefficients in high frequency significantly, which would be more advantageous for quantification coding [2]. [Copyright &y& Elsevier]

Abstract: According to the characteristic of large aperture static interference imaging spectrometer (LASIS), a non-linear orientation prediction three-dimensional (3D) wavelet transform method is proposed in this paper on the basis of the 3D orientation prediction wavelet transform method proposed by Li, Ma and Wu in 2008 [1]. The method proposed in this paper still combines directional prediction into 3D lifting wavelet transformation, but compared with the 3D orientation prediction wavelet transform method, it made a breakthrough in the limitation that the orientation predicted must be a straight linear direction. The experimental results showed that this method improved the performance of wavelet obviously, especially on the LASIS image with quite severe and unstable directional characteristic as seen in the study of Ma and Ma (2009) [2]. Meanwhile, the characteristic of spectrum image recovered by the proposed method also possessed better performance. [Copyright &y& Elsevier]

Abstract: In this paper, a high efficient adaptive LBG VQ (vector quantization) method is proposed, which is an improved version of the expansion LBG VQ method [1] (proposed in 2009)and exploits the shortest distance from a point to line segment. Moreover, the proposed method is used on the lossless compression of LASIS (Large Aperture Static Imaging Spectrometer) interferential multi-spectral images. Compared with the expansion LBG VQ method proposed in 2009, the experimental results showed that the adaptive LBG VQ method improved the compression performance obviously. And the experiment also showed that when using these LBG VQ methods above without Dual-Direction Prediction [2], the entropy generated by expansion LBG VQ method [1] sometimes will be larger than that generated by traditional LBG VQ method [3], and when using these LBG VQ methods above after Dual-Direction Prediction [2], CR (compression ratio) generated by expansion LBG VQ method [1] sometimes will be less than that generated by traditional LBG VQ method [3], while the entropy generated by the method proposed in this paper will be much less than that generated by either traditional LBG VQ method [3] or the expansion LBG VQ method [1], and CR generated by the method proposed in this paper will be much larger than that generated by either traditional LBG VQ method [3] or the expansion LBG VQ method [1]. [ABSTRACT FROM AUTHOR]

Ultrafast laser drilling has been proven to effectively reduce the heat-affected zone (HAZ) of carbon fiber-reinforced polymer (CFRP) composites. However, previous research mainly focused on the effects of picosecond laser parameters on CFRP drilling. Compared with a picosecond laser, a femtosecond laser can achieve higher quality CFRP drilling due to its smaller pulse width, but there are few studies on the effects of femtosecond laser parameters on CFRP drilling. Moreover, the cross-sectional taper of CFRP produced by laser drilling is very large. This paper introduces the use of the femtosecond laser to drill cylindrical holes in CFRP. The effect of laser power, rotational speed of the laser, and number of spiral passes on HAZ and ablation depth in circular laser drilling and spiral laser drilling mode was studied, respectively. It also analyzed the forming process of the drilling depth in the spiral drilling mode and studied the influence of laser energy and drilling feed depth on the holes' diameters and the taper. The experimental results show that the cylindrical hole of CFRP with a depth-to-diameter ratio of about 3:1 (taper < 0.32°, HAZ < 10 μm) was obtained by using femtosecond laser and a spiral drilling apparatus. [ABSTRACT FROM AUTHOR]

Phase retrieval occurs in many research areas. There are some classical phase retrieval methods such as hybrid input-output (HIO) and difference map (DM). However, phase retrieval results are sensitive to noise, and the reconstructed images always include artefacts. In this paper, we use the DM algorithm together with DNN to get better phase retrieval results. We train one deep neural network using amplitude images and phase images, respectively. First, using DM, we get initial reconstructed amplitude and phase results. Then, using DNN improves both amplitude and phase results. Finally, using the DM algorithm again improves the DNN results further. The numerical experimental results show that using DM gives better results than HIO, and using DNN improves phase information better than just using DNN to train for amplitude information alone. Compared with only using DNN improves amplitude methods, our method using DM plus DNN plus DM yields a better reconstruction performance for both amplitude and phase. [ABSTRACT FROM AUTHOR]

Airborne aiming pod is mainly used for searching, capturing and tracking the target indicated by the laser, and then dropping the laser-guided weapons precisely to complete the close-range support attack or dual-aircraft cooperative instruction/attack. High-precision laser beam pointing technology is the core of the airborne aiming pod, and the accuracy of it is fatal for the realization of the final tactical index. In this paper, the factors affecting the beam pointing accuracy based on the research of airborne aiming pod platform are analyzed. At first, the composition of the pod system and the motion coupling of the composite axis pod are introduced; and then the beam pointing algorithm under the external guidance combined with aircraft disturbance and the effects of atmospheric turbulence and atmospheric attenuation on beam quality, including optical axis drift, beam spread and intensity distribution variation are discussed. Finally, the performance of the pod servo system is simulated and verified in Simulink. The simulation result of the real flight situation with disturbance moment shows that the pointing accuracy of the airborne aiming pod can reach 0.0075 degrees. The data analyzed in this paper can provide technical reference for other aiming photoelectric platforms. [ABSTRACT FROM AUTHOR]

In this paper, one active polarization imaging technique is introduced to enhance the contrast between the target and the sky to overcome the environmental interference such as cloud and fog in the long-range detection of air vehicles. At first, the theory of the whole system, the selection of detection system and the wavelength of laser, and the brief design of the optical system are described. Secondly the selection of the laser power is derived based on the elements such as atmosphere attenuation, and the sensitivity, image contrast and signal-to-noise ratio in the detector. Then the polarization imaging mechanism of the full aperture polarization measurement platform is analyzed. Finally a nonlinear differentiator is proposed to improve the speed stability in the tracking to ensure high imaging quality. The purpose of this paper is to lay a theoretical foundation for active polarization imaging technology and to promote its engineering application in shooting range and other fields. [ABSTRACT FROM AUTHOR]

Fourier ptychographic microscopy (FPM) has risen as a promising computational imaging technique that breaks the trade-off between high resolution and large field of view (FOV). Its reconstruction is normally formulated as a blind phase retrieval problem, where both the object and probe have to be recovered from phaseless measured data. However, the stability and reconstruction quality may dramatically deteriorate in the presence of noise interference. Herein, we utilized the concept of alternating direction method of multipliers (ADMM) to solve this problem (termed ADMM-FPM) by breaking it into multiple subproblems, each of which may be easier to deal with. We compared its performance against existing algorithms in both simulated and practical FPM platform. It is found that ADMM-FPM method belongs to a global optimization algorithm with a high degree of parallelism and thus results in a more stable and robust phase recovery under noisy conditions. We anticipate that ADMM will rekindle interest in FPM as more modifications and innovations are implemented in the future. [ABSTRACT FROM AUTHOR]

This paper proposes a novel image data augmentation method based on the 4F optical diffuser system with two spatial light modulators. The method combines data augmentation methods based on geometric transformations with optical phase diffuser, resulting in a system which is effective and easy-to-use. This method can be used in an all-optical machine learning system or in a hybrid digital-optical system, and its digital implementation can also be used in learning-based computer vision applications. The method was tested in image classification of MNIST dataset. It was demonstrated that the augmented dataset significantly improves the classification accuracy. [ABSTRACT FROM AUTHOR]

For realizing the engineering application of key technologies in the long-range active polarization imaging system, an experimental platform is built to study the static active polarization imaging technology and related image fusion algorithm in the smoke and dust environment. In the experiments, 532 nm CW laser is used as illumination source after collimation and beam expansion, metal bullet model is used as detection target, smoke environment is created through smoke generator in organic glass hood, polarization direction can be rotated by the polarizer, the images are collected by Lumerera-LM135 camera. The analysis and handling on the images are conducted by the use of the MATLAB software. In the aspects of image information entropy, average gradient, spatial frequency and standard deviation, the polarization imaging effects in various environments are quantitatively compared and analyzed, and the imaging advantages of laser illumination and polarization imaging technology in smoke and dust environment and dim conditions are verified. In order to further improve the imaging quality of active polarization imaging technology, a polarization image fusion algorithm based on fuzzy adaptive is proposed after analyzing a large number of data acquired in the experiment. The experimental results show that the active polarization imaging technology and the improved image fusion algorithm can improve the detection distance of optical system and image contrast, expand the digestible information of images for the long-range dim target imaging in low visibility environment, and provide important data reference and technology support for the application of this technology in shooting range. [ABSTRACT FROM AUTHOR]

• The mechanism of an imperceptible artifact caused by edge effect is discovered. • Our PPSID-FPM algorithm can remove the edge effect induced artifacts, improves the standard deviation of phase accuracy as a factor of 4 from 0.08 radians to 0.02 radians, which is around λ/400. • The computation time of PPSID-FPM algorithm is comparable to conventional FPM and it may be extended to other fourier analysis techniques. Fourier ptychographic microscopy (FPM) is a promising computational imaging technique with high resolution, wide field-of-view (FOV) and quantitative phase recovery. So far, a series of system errors that may corrupt the image quality of FPM has been reported. However, an imperceptible artifact caused by edge effect caught our attention and may also degrade the precision of phase imaging in FPM with a cross-shape artifact in the Fourier space. We found that the precision of reconstructed phase at the same subregion depends on the different sizes of block processing as a result of different edge conditions, which limits the quantitative phase measurements via FPM. And this artifact is caused by the aperiodic image extension of fast Fourier transform (FFT). Herein, to remove the edge effect and improve the accuracy, two classes of opposite algorithms termed discrete cosine transform (DCT) and periodic plus smooth image decomposition (PPSID) were reported respectively and discussed systematically. Although both approaches can remove the artifacts in FPM and may be extended to other Fourier analysis techniques, PPSID-FPM has a comparable efficiency to conventional FPM algorithm. The PPSID-FPM algorithm improves the standard deviation of phase accuracy as a factor of 4 from 0.08 radians to 0.02 radians. Finally, we summarized and discussed all the reported system errors of FPM within a generalized model. [ABSTRACT FROM AUTHOR]

To evaluate the effect of phase change materials (PCMs) on the indoor thermal environment of greenhouses under different weather conditions and over a long time in the heating season, a study was carried out using both experimental method and numerical method. The study was conducted in a typical greenhouse located in Beijing, China, and important parameters have been monitored continuously for 61 days, including indoor air temperature, outdoor air temperature, solar radiation, surface temperature of greenhouse envelopes and soil temperature. Based on these parameters, a number of indicators, namely, operative temperature, daily effective accumulative temperature, irradiated surface temperature of the north wall, average temperature of PCMs, and daily heat storage and release, have been used to evaluate the performance of PCMs in greenhouses. All indicators have provided consistent results that confirm the positive effect of PCMs on improving the indoor thermal environment of greenhouses over a long time. Additionally, the paper has demonstrated that a sunny weather could help to promote the efficiency of PCMs, comparing to a cloudy weather. [ABSTRACT FROM AUTHOR]