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In the field of machine vision, depth segmentation plays a crucial role in dividing targets into different regions based on abrupt changes in depth. Phase-shifting depth segmentation is a technique that extracts singular points to form segmentation lines by leveraging the phase-shifting invariance of singular points in different wrapped phase maps. This makes it immune to color, texture, and camera exposure. However, current phase-shifting depth segmentation techniques face challenges in the precision of segmentation. To overcome this issue, this paper proposes a singular points extraction technique by constructing a more comprehensive threshold with the help of the minimum period of the phase map. Taking full advantage of the proposed technique, mean-value points and order singular points are accurately filtered out, and the integrity of segmentation lines in high-curvature regions can be guaranteed. During optimization processing, the precision of segmentation is improved by employing a low-cost morphology-based optimization model. Simulation results demonstrate the segmentation accuracy reaches up to 98.58% even in a noisy condition. Experimental results on different objects indicate that the proposed method exhibits good generalization and robustness.
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Optical zoom is an essential function for many imaging systems including consumer electronics, biomedical microscopes, telescopes, and projectors. However, most optical zoom imaging systems have discrete zoom rates or narrow zoom ranges. In this work, a continuous optical zoom imaging system with a wide zoom range is proposed. It consists of a solid lens, two Alvarez lenses, and a camera with an objective. Each Alvarez lens is composed of two cubic phase plates, which have inverted freeform surfaces concerning each other. The movement of the cubic phase masks perpendicular to the optical axis is realized by the actuation of the dielectric elastomer. By applying actuation voltages to the dielectric elastomer, cubic phase masks are moved laterally and then the focal lengths of the two Alvarez lenses are changed. By adjusting the focal lengths of these two Alvarez lenses, the optical magnification is tuned. The proposed continuous optical zoom imaging system is built and the validity is verified by the experiments. The experimental results demonstrate that the zoom ratio is up to 10×, i.e., the magnification continuously changes from 1.58× to 15.80× when the lateral displacements of the cubic phase masks are about 1.0 mm. The rise and fall response times are 150â ms and 210â ms, respectively. The imaging resolution can reach 114 lp/mm during the optical zoom process. The proposed continuous optical imaging system is expected to be used in the fields of microscopy, biomedicine, virtual reality, etc.
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Instantaneous phase shifting interferometry technology, the core component of which is the pixel micropolarizer camera, has been widely used in commercial interferometers. This technology has the superiority of single-frame acquisition, vibration insensitivity, and no need for phase shifting devices. However, due to manufacturing defects and accuracy limitations, the extinction ratios (ER) of the micropolarizer array are different and fairly small, directly affecting the phase calculation accuracy. This paper initially derives a theoretical expression for the phase calculation error introduced by the extinction ratio (ER) and proposes the error correction model to reduce phase calculation errors caused by the extinction ratio. The theoretical analysis can serve as an important basis for accurately assessing the polarization characteristics of a pixel micropolarizer camera. Quantifying the impact of the extinction ratios provides significant support for the selection of polarization equipment. In addition, the paper proposes a calibration model to improve measurement accuracy, which can serve as an effective means to reduce the impact of the extinction ratio (ER). The innovative research content revealed the influence of extinction ratio (ER), serving as a valuable complement to the existing analysis and research on extinction ratio (ER).
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Topographic measurements of micro- or nanostructures are essential in cutting-edge scientific disciplines such as optical communications, metrology, and structural biology. Despite the advances in surface metrology, measuring micron-scale steps with wide field of view (FOV) and high-resolution remains difficult. This study demonstrates a dual-wavelength Fourier ptychographic microscopy for high-resolution topographic measurement across a wide FOV using an aperture scanning structure. This structure enables the capture of a three-dimensional (3D) sample's scattered field with two different wavelength lasers, thus allowing the axial measurement range growing from nano- to micro-scale with enhanced lateral resolution. To suppress the unavoidable noises and artifacts caused by temporal coherence, system vibration, etc., a total variation (TV) regularization algorithm is introduced for phase retrieval. A blazed grating with micron-scale steps is used as the sample to validate the performance of our method. The agreement between the high-resolution reconstructed topography with our method and that with atomic force microscopy verified the effectiveness. Meanwhile, numerical simulations suggest that the method has the potential to characterize samples with high aspect-ratio steps.
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Ghost imaging (GI) has been widely used in the applications including spectral imaging, 3D imaging, and other fields due to its advantages of broad spectrum and anti-interference. Nevertheless, the restricted sampling efficiency of ghost imaging has impeded its extensive application. In this work, we propose a novel foveated pattern affine transformer method based on deep learning for efficient GI. This method enables adaptive selection of the region of interest (ROI) by combining the proposed retina affine transformer (RAT) network with minimal computational and parametric quantities with the foveated speckle pattern. For single-target and multi-target scenarios, we propose RAT and RNN-RAT (recurrent neural network), respectively. The RAT network enables an adaptive alteration of the fovea of the variable foveated patterns spot to different sizes and positions of the target by predicting the affine matrix with a minor number of parameters for efficient GI. In addition, we integrate a recurrent neural network into the proposed RAT to form an RNN-RAT model, which is capable of performing multi-target ROI detection. Simulations and experimental results show that the method can achieve ROI localization and pattern generation in 0.358â ms, which is a 1 × 105 efficiency improvement compared with the previous methods and improving the image quality of ROI by more than 4â dB. This approach not only improves its overall applicability but also enhances the reconstruction quality of ROI. This creates additional opportunities for real-time GI.
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The Ritchey-Common test is widely adopted to measure large optical flats. The traditional Ritchey-Common test eliminates the defocus error with multiple tests by changing the position of the mirrors, which suffers from cumbersome steps, poor repeatability, coupled system error, extra mirror deformation, and potential overturning. The above problems increase the test time, decrease the reliability and accuracy, increase the test cost, and threaten manufacturing safety. We propose a single-test Ritchey-Common interferometry to avoid the obligatory position change in the traditional method. A sub-aperture of test flat is directly measured by a small-aperture interferometer before the test, which is easy to implement, to replace the extra system wavefront measurement in different positions. The defocus is calculated in sub-aperture at exactly the same position as the full-field measurement without the position change, then the surface form under test can be obtained with accurate optical path modeling. Measurement experiments for 100 mm and 2050â mm aperture flats were performed to demonstrate the feasibility of this method. Compared with a direct test in a standard Zygo interferometer, the peak to valley (PV) and root mean square (RMS) errors were 0.0889 λ and 0.0126 λ (λ=632.8â nm), respectively, which reaches the upper limit of accuracy of the interferometer. To the best of our knowledge, this is the first proposal of the Ritchey-Common test that can eliminate the defocus error and realize high accuracy measurement in a single test. Our work paves the way for reliable and practical optical metrology for large optical flats.
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For the high-precision fabrication of a continuous phase plate (CPP), a combined decoupling algorithm of single-step decoupling based on the Clairaut-Schwarz theorem and global decoupling by stagewise iteration is proposed. It attempts to address the problem of the low accuracy and limitation of the existing slope-based figuring (SF) model in two-dimensional applications caused by the vector removal coupling between the tool slope influence function and the material removal slope due to the inherent convolution effect in the SF model. The shortcomings of CPP interferometry and the application bottleneck of the Hartmann test in traditional height-based figuring model are studied. The generation mechanism of vector removal coupling is analyzed and compensated. A CPP of 85m m×85m m was successfully machined by the decoupled slope-based figuring model, and the root mean square (RMS) of the surface height error accounted for 6.01% of the RMS of the design value. The research results can effectively improve the convergence and certainty of CPP fabrication using the slope-based figuring model.
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Fringe projection profilometry has gained significant interest due to its high precision, enhanced resolution, and simplified design. Typically, the spatial and perspective measurement capability is restricted by the lenses of the camera and projector in accordance with the principles of geometric optics. Therefore, large-size object measurement requires data acquisition from multiple perspectives, followed by point cloud splicing. Current point cloud registration methods usually rely on 2D feature textures, 3D structural elements, or supplementary tools, which will increase costs or limit the scope of the application. To address large-size 3D measurement more efficiently, we propose a low-cost and feasible method that combines active projection textures, color channel multiplexing, image feature matching and coarse-to-fine point registration strategies. Using a composite structured light with red speckle patterns for larger areas and blue sinusoidal fringe patterns for smaller ones, projected onto the surface, which allows us to accomplish simultaneous 3D reconstruction and point cloud registration. Experimental results demonstrate that the proposed method is effective for the 3D measurement of large-size and weak-textured objects.
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A multimode detection system has stringent requirements in terms of electromagnetic characteristic control and electromagnetic compatibility. To meet these requirements, we designed and manufactured a type of transparent electromagnetic-wave-absorbing optical window based on a random grid (EAOWRG) in this study. Owing to the design and regulation of the materials of the random grid and the structures of the metasurface, the optical window has excellent multispectral transparency, electromagnetic wave absorption, and electromagnetic shielding performance. The experimental results showed that the transmissivity of the EAOWRG in the optical spectral ranges of 460-800â nm and 8-12 µm is above 89.77%, the electromagnetic reflectivity in the frequency ranges of 3.6-7.2â GHz and 14.3-17.7â GHz is not more than - 5â dB, the bandwidth at which the electromagnetic reflectivity is not more than -10â dB is 4.4â GHz, the electromagnetic shielding effectiveness in the frequency range of 2-18â GHz is above 31â dB. The average radar cross section of the detection system using the EAOWRG in the ± 60° angle domain at 6â GHz is 8.79â dB lower than that before processing. The detection system has a good imaging effect in the visible and infrared bands, meeting the requirements of the electromagnetic characteristic control and electromagnetic compatibility, and has good application prospects.
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Fringe projection profilometry (FPP) has been widely used in 3D measurement due to its high precision and non-contact properties. Nevertheless, it still faces great challenges in measuring scenes with complex reflectivity, in which the dynamic range of the reflected light field of the scene is significantly higher than that of the image detector. In this paper, we propose a time-slicing strategy for high dynamics range 3D imaging by projecting a series of sinusoidal fringe patterns with short and equal length exposure time and performing the fusion of different numbers of short exposure images according to the local gray-value distribution of the images. Moreover, to further improve the measurement efficiency, we realize phase unwrapping using complementary Gray code patterns, which are binary and insensitive to the image sensor's nonlinear response to the reflected light from the scene under test. Experiments are conducted to demonstrate the feasibility and efficiency of the proposed method.
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Panoramic ghost imaging (PGI) is a novel method by only using a curved mirror to enlarge the field of view (FOV) of ghost imaging (GI) to 360°, making GI a breakthrough in the applications with a wide FOV. However, high-resolution PGI with high efficiency is a serious challenge because of the large amount of data. Therefore, inspired by the variant-resolution retina structure of human eye, a foveated panoramic ghost imaging (FPGI) is proposed to achieve the coexistence of a wide FOV, high resolution and high efficiency on GI by reducing the resolution redundancy, and further to promote the practical applications of GI with a wide FOV. In FPGI system, a flexible variant-resolution annular pattern structure via log-rectilinear transformation and log-polar mapping is proposed to be used for projection, which can allocate the resolution of the region of interest (ROI) and the other region of non-interest (NROI) by setting related parameters in the radial and poloidal directions independently to meet different imaging requirements. In addition, in order to reasonably reduce the resolution redundancy and avoid the loss of the necessary resolution on NROI, the variant-resolution annular pattern structure with a real fovea is further optimized to keep the ROI at any position in the center of 360° FOV by flexibly changing the initial position of the start-stop boundary on the annular pattern structure. The experimental results of the FPGI with one fovea and multiple foveae demonstrate that, compared to the traditional PGI, the proposed FPGI not only can improve the imaging quality on the ROIs with a high resolution and flexibly remain a lower-resolution imaging on the NROI with different required resolution reduction; but also reduce the reconstruction time to improve the imaging efficiency due to the reduction of the resolution redundancy.
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A continuous time-domain adaptive power model of transmitter optical and control algorithm based on atmospheric turbulence channel reciprocity are explored for mitigating the free-space optical communication (FSOC) receiver optical intensity scintillation and bit error rate (BER) deterioration. First, a transmitter optical adaptive power control (OAPC) system architecture using four wavelength optical signals based on atmospheric turbulence channel reciprocity is proposed, and electronically variable optical attenuator (EVOA) and erbium-doped fiber amplifier (EDFA) are employed as the main OAPC units for power adaptation. Moreover, a reciprocity evaluation model for gamma-gamma (G-G) continuous-time signals is generated using the autoregressive moving average (ARMA) stochastic process, which takes into account the delay time and system noise, and a reciprocity-based OPAC algorithm is proposed. Numerical simulations were also performed to analyze the signal reciprocity characteristics under different turbulence, noise, and sampling time mismatch at both ends, as well as the scintillation index (SI) performance under OAPC system operation. Simultaneously, the time-domain signals of continuous quadrature amplitude modulation -16 (QAM-16) and QAM-32 real states are fused with the gamma-gamma (G-G) reciprocal turbulence continuous signals to analyze the probability density function (PDF) and bit error ratio (BER) performance after OAPC correction. Finally, a 64 Gpbs QAM-16 OPAC communication experiment was successfully executed based on an atmospheric turbulence simulator. It is shown that the OAPC correction is carried out using reciprocity at millisecond sampling delay, the light intensity scintillation of the communication signal can be well suppressed, the signal-to-noise ratio (SNR) is greatly improved, the suppression is more obvious under strong turbulence, the overall BER reduction is greater than 2.8 orders of magnitude with the OAPC system, and this trend becomes more pronounced as the received power increases, even reach 6 orders of magnitude in some places. This work provides real time-domain continuous signal samples for real signal generation of communication signals in real turbulence environments, adaptive coding modulation using reciprocity, channel estimation, and optical wavefront adaptive suppression, which are the basis of advanced adaptive signal processing algorithms.
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The wireless transmission of video data mainly entails addressing the massive video stream data and ensuring the quality of image frame transmission. To reduce the amount of data and ensure an optimal data transmission rate and quality, we propose a free-space optical video transmission system that applies compressed sensing (CS) algorithms to wireless optical communication systems. Based on the Artix-7 series field programmable gate array (FPGA) chip, we completed the hardware design of the optical wireless video transceiver board; the CS image is transmitted online to the FPGA through Gigabit Ethernet, and the video data is encoded by gigabit transceiver with low power (GTP) and converted into an optical signal, which is relayed to the atmospheric turbulence simulation channel through an attenuator and a collimating mirror. After the optical signal is decoded by photoelectric conversion at the receiving end, the Camera-Link frame grabber is d; thus, the image is collected, and it is reconstructed offline. Herein, the link transmission conditions of different algorithm sampling rates, optical power at the receiving end, and atmospheric coherence length are measured. The experimental results indicate that the encrypt-then-compress (ETC) type algorithm exhibits a more optimal image compression transmission reconstruction performance, and that the 2D compressed sensing (2DCS) algorithm exhibits superior performance. Under the condition that the optical power satisfies the link connectivity, the PSNR value of the reconstructed image is 3-7â dB higher than that of the comparison algorithm. In a strong atmosphere turbulence environment, the peak signal-to-noise ratio (PSNR) of the corresponding reconstructed image under different transmission rates at the receiving end can still exceed 30â dB, ensuring the complete reconstruction of the image.
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Optical coherence tomography (OCT) images are commonly affected by sidelobe artifacts due to spectral non-uniformity and spectral leakage. Conventional frequency domain spectral shaping methods widen the mainlobe and compromise axial resolution. While image-domain deconvolution techniques can address the trade-off between axial resolution and artifact suppression, their reconstruction quality relies on accurate measurement or estimation of system point spread function (PSF). Inaccurate PSF estimation leads to loss of details in the reconstructed images. In this Letter, we introduce multi-shaping sparse-continuous reconstruction (MSSCR) for an OCT image, a novel, to the best of our knowledge, framework that combines spectral multi-shaping and iterative image reconstruction with sparse-continuous priors. The MSSCR achieves sidelobe suppression without requiring any PSF measurement or estimation and effectively preserving the axial resolution. The experimental results demonstrate that the MSSCR achieves sidelobe suppression of more than 8â dB. We believe that the MSSCR holds potential for addressing sidelobe artifacts in OCT.
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We propose a single-shot quantitative differential phase contrast method with polarization multiplexing illumination. In the illumination module of our system, a programmable LED array is divided into four quadrants and covered with polarizing films of four different polarization angles. We use a polarization camera with polarizers before the pixels in the imaging module. By matching the polarization angle between the polarizing films over the custom LED array and the polarizers in the camera, two sets of asymmetric illumination acquisition images can be calculated from a single-shot acquisition image. Combined with the phase transfer function, we can calculate the quantitative phase of the sample. We present the design, implementation, and experimental image data demonstrating the ability of our method to obtain quantitative phase images of a phase resolution target, as well as Hela cells.
Assuntos
Iluminação , Humanos , Células HeLa , Microscopia de Contraste de FaseRESUMO
Fringe projection profilometry (FPP) has attracted wide attention in optical 3D measurement field for its high resolution, high accuracy, and simple system construction. However, the inherent requirements of FPP's measurement speed and accuracy are contradictory. We adopt a 3+1 phase retrieval strategy combining phase shift and the Hilbert transform algorithm to achieve high-speed, high-precision simultaneous measurements with only four binary fringe patterns. Furthermore, when using temporal phase unwrapping, fringe order errors (FOEs) are inevitable. Although these FOEs do not affect neighboring pixels, they are brought into the final point cloud coordinates by the absolute phase value. We propose a fringe order correction method to eliminate FOEs. The feasibility and effectiveness of this method are verified by the experimental results.
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The radius of curvature (R) is a fundamental parameter of spherical optical surfaces. The measurement range of the widely adopted traditional interferometric method is limited by the length of the precision linear guide rail carrying the measured surface from the cat's eye to the confocal position, and the test result is vulnerable to airflow and vibration in the test environment. An interferometric method is proposed for the radius measurement of spherical surfaces based on a small axial moving distance and the corresponding defocus wavefront to eliminate the dependence on a long guide rail and extend the measuring range. To eliminate the influence of the test environment and calculate the R, a defocus transform algorithm is proposed to instantaneously measure the defocus wavefront from a single interferogram. Numerical simulations theoretically demonstrate that there is no limit to the measurement range of this method because only a short distance of the measured mirror must be moved. A spherical mirror with a radius of curvature of 101.6087 mm is experimentally tested, and the relative measurement error is 0.037%. This method can achieve high accuracy for optical shops and greatly increase the measurement range of the interferometric method without additional equipment.
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The ranging accuracy of pulsed time-of-flight (TOF) lidar is affected by walk error and jitter error. To solve the issue, the balanced detection method (BDM) based on fiber delay optic lines (FDOL) is proposed. The experiments are carried out to prove the performance improvement of BDM over the conventional single photodiode method (SPM). The experimental results show that BDM can suppress common mode noise and simultaneously shift the signal to high frequency, which reduces the jitter error by approximately 52.4% and maintains the walk error at less than 300 ps with a non-distorted waveform. The BDM can be further applied to silicon photomultipliers.
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With the development of infrared detection technology and the improvement of military remote sensing needs, infrared object detection networks with low false alarms and high detection accuracy have been a research focus. However, due to the lack of texture information, the false detection rate of infrared object detection is high, resulting in reduced object detection accuracy. To solve these problems, we propose an infrared object detection network named Dual-YOLO, which integrates visible image features. To ensure the speed of model detection, we choose the You Only Look Once v7 (YOLOv7) as the basic framework and design the infrared and visible images dual feature extraction channels. In addition, we develop attention fusion and fusion shuffle modules to reduce the detection error caused by redundant fusion feature information. Moreover, we introduce the Inception and SE modules to enhance the complementary characteristics of infrared and visible images. Furthermore, we design the fusion loss function to make the network converge fast during training. The experimental results show that the proposed Dual-YOLO network reaches 71.8% mean Average Precision (mAP) in the DroneVehicle remote sensing dataset and 73.2% mAP in the KAIST pedestrian dataset. The detection accuracy reaches 84.5% in the FLIR dataset. The proposed architecture is expected to be applied in the fields of military reconnaissance, unmanned driving, and public safety.
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In a data-driven context, bionic polarization navigation requires a mass of skylight polarization pattern data with diversity, complete ground truth, and scene information. However, acquiring such data in urban environments, where bionic polarization navigation is widely utilized, remains challenging. In this paper, we proposed a virtual-real-fusion framework of the skylight polarization pattern simulator and provided a data preparation method complementing the existing pure simulation or measurement method. The framework consists of a virtual part simulating the ground truth of skylight polarization pattern, a real part measuring scene information, and a fusion part fusing information of the first two parts according to the imaging projection relationship. To illustrate the framework, we constructed a simulator instance adapted to the urban environment and clear weather and verified it in 174 urban scenes. The results showed that the simulator can provide a mass of diverse urban skylight polarization pattern data with scene information and complete ground truth based on a few practical measurements. Moreover, we released a dataset based on the results and opened our code to facilitate researchers preparing and adapting their datasets to their research targets.