Spatially varying defocus map estimation from a single image based on spatial aliasing sampling method.

In current optical systems, defocus blur is inevitable due to the constrained depth of field. However, it is difficult to accurately identify the defocus amount at each pixel position as the point spread function changes spatially. In this paper, we introduce a histogram-invariant spatial aliasing sampling method for reconstructing all-in-focus images, which addresses the challenge of insufficient pixel-level annotated samples, and subsequently introduces a high-resolution network for estimating spatially varying defocus maps from a single image. The accuracy of the proposed method is evaluated on various synthetic and real data. The experimental results demonstrate that our proposed model outperforms state-of-the-art methods for defocus map estimation significantly.

Skylight Polarization Pattern Simulator Based on a Virtual-Real-Fusion Framework for Urban Bionic Polarization Navigation.

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.

Ultralow loss hollow-core negative curvature fibers with nested elliptical antiresonance tubes.

Hollow-core negative curvature fibers can confine light within air core and have small nonlinearity and dispersion and high damage threshold, thereby attracting a great deal of interest in the field of hollow core fibers. However, reducing the loss of hollow-core negative curvature fibers is a serious problem. On this basis, three new types of fibers with different nested tube structures are proposed in the near-infrared spectral regions and compared in detail with a previously proposed hollow-core negative curvature fiber. We used finite-element method for numerical simulation studies of their transmission loss, bending loss, and single-mode performance, and then the transmission performance of various structural fibers is compared. We found that the nested elliptical antiresonant fiber 1 has better transmission performance than that of the three other types of fibers in the spectral range of 0.72-1.6 µm. Results show that the confinement loss of the LP01 mode is as low as 6.45×10-6 dB/km at λ = 1.06 µm. To the best of our knowledge, the record low level of confinement loss of hollow-core antiresonant fibers with nested tube structures was created. In addition, the nested elliptical antiresonant fiber 1 has better bending resistance, and its bending loss was below 2.99×10-2 dB/km at 5 cm bending radius.

Nondestructive testing and 3D imaging of PE pipes using terahertz frequency-modulated continuous wave.

Polyethylene (PE) pipes are widely used as the main carrier for the transportation of natural gas, so nondestructive testing techniques for PE pipes are essential for the safety of natural gas transportation. In order to compensate for the shortcomings of conventional inspection methods, a terahertz (THz) three-dimensional imaging system for nondestructive inspection of PE pipes is designed. The system is based on frequency-modulated continuous-wave (FMCW) technology, with a THz source bandwidth of 0.225-0.330 THz and an output power of over 5 mW, which can achieve submillimeter spatial resolution in three dimensions. The system is used to scan PE pipes in three dimensions in a laboratory environment, and the results show that the system could achieve nondestructive testing and three-dimensional imaging of different defects in PE pipes. In addition, combined with the deep-learning-based THz transformer network, the intelligent identification of different defects is realized, and the accuracy rate can reach up to 88%. The above results provide technical guidance for the application of THz FMCW systems in the actual detection of PE pipes, and provide supplements and improvements for traditional detection methods.

Window filtering algorithm for a low repetition rate pulsed laser coherent combination system.

The multi-dithering method has been well verified in the phase-locking of polarization coherent combination experiments. However, it is difficult to apply to low repetition rate pulsed laser coherent combination, since there exists an overlap in the frequency domain between the pulse laser and the large amplitude-phase noise resulting in traditional filters being unable to effectively separate the phase noise. Aiming to solve the problem, we propose, to the best of our knowledge, a novel method of pulse noise detection, identification, and filtering based on the autocorrelation characteristics between noise signals. The self-designed adaptive window filtering algorithm can effectively filter the pulse signal doped in the phase noise around 0.1 ms. After the pulses are filtered out, the remaining phase noise signal is used as the input signal of the multi-dithering method for phase locking; the phase difference of two pulsed beams (10 kHz) is successfully compensated to zero; and the coherent combination of the closed-loop phase lock is realized. Simultaneously, the phase correction periods are short, the phase lock effect is stable, and the intensity of the final combined pulses rises to the ideal value (0.9Imax). In addition, the adaptive window filtering algorithm we proposed can be applied to the coherent combined system of large array fiber lasers and further lay the foundation for fiber phased array lidar.

MTF improvement for optical synthetic aperture system via mid-frequency compensation.

Optical synthetic aperture imaging system has grown out the quest for higher angular resolution in astronomy, which combines the radiation from several small sub-apertures to obtain a resolution equivalent to that of a single filled aperture. Due to the discrete distribution of the sub-apertures, pupil function is no longer a connected domain, which further leads to the attenuation or loss of the mid-frequency modulation transfer function (MTF). The mid-frequency MTF compensation is therefore a key focus. In this paper, a complete mid-frequency compensation algorithm is proposed, which can extract and fuse the frequency of different synthetic aperture systems and monolithic aperture systems according to their special MTF characteristics. The dimensions of the monolithic aperture and optical synthetic aperture system are derived, and the longest baseline of the monolithic aperture is much smaller than that of the optical synthetic aperture system. Then the separated spatial frequency information is extracted and synthesized according to the spatial frequency equivalence point. Finally, the full-frequency enhanced image is recovered by using improved Wiener-Helstrom filter, which adopts specific parameters based on different sub-aperture arrangements. The mid-frequency MTF of Golay-3 increases from 0.12 to 0.16 and that of Golay-6 increases from 0.06 to 0.18. Both the simulation and experiment prove that the proposed method not only realizes the spatial resolution determined by the longest baseline of the optical synthetic aperture system, but also successfully compensates its mid-frequency MTF.

Optimized Golay-9 array configurations for mid-frequency compensation in optical sparse aperture systems.

Optical sparse aperture (OSA) imaging systems show great potential to generate higher resolution images than those of equivalent single filled aperture systems. However, due to the sparsity and dispersion of sparse aperture arrays, pupil function is no longer a connected domain, which further attenuates or loses the mid-frequency modulation transfer function (MTF), resulting in lower mid-frequency contrast and blurred images. Therefore, an improved traversal algorithm is proposed to optimize Golay-9 array configurations for compensating the mid-frequency MTF. Its structural parameters include diameters of sub-apertures, relative rotation angles between individual sub-apertures, and radius of concentric circles. Then, these parameters are traversed successively in order. Finally, the influences of the obtained optimized array configurations on the mid-frequency MTF are analyzed in detail, and the image performances are evaluated. The experimental results prove the contrast enhancement. Compared with a Golay-9 array at F=36.5%, the maximum MTF increases from 0.1503 to 0.307, and the mid-frequency MTF is boosted from 0.0565 to 0.0767. In addition, the peak signal to noise ratio of the degraded image is promoted from 19.75 dB to 20.63 dB. Both quantitative and qualitative evaluations demonstrate the validity of the proposed method.

Enhancement of terahertz waves from two-color laser-field induced air plasma excited using a third-color femtosecond laser.

This study experimentally demonstrates and theoretically analyzes the enhancement of terahertz (THz) waves from two-color laser-field (consisting of a near-infrared femtosecond laser and its second-harmonic wave) induced air plasma using an additional 800 nm femtosecond laser. The experiments revealed that the additional 800 nm laser increased the THz energy up to 22 times. To understand the enhancement mechanism and reveal the maximum enhancement conditions, the effects of the 800 nm beam's polarization and energy variations of both beams on the THz amplification were studied. With the increase in the 800 nm pulse energy, the THz yield initially increases, and then decreases after reaching an inflection point. The THz increase rate continues to increase with the decrease in energy of the near-infrared two-color fields. The 800 nm beam could efficiently modulate the THz spectral energy distribution by increasing the high-frequency components, while decreasing the low-frequency components.

Image restoration for synthetic aperture systems with a non-blind deconvolution algorithm via a deep convolutional neural network.

Optical synthetic aperture imaging systems, which consist of in-phase circular sub-mirrors, can greatly improve the spatial resolution of a space telescope. Due to the sub-mirrors' dispersion and sparsity, the modulation transfer function is decreased significantly compared to a fully filled aperture system, which causes obvious blurring and loss of contrast in the collected image. Image restoration is the key to get the ideal clear image. In this paper, an appropriative non-blind deconvolution algorithm for image restoration of optical synthetic aperture systems is proposed. A synthetic aperture convolutional neural network (CNN) is trained as a denoiser prior to restoring the image. By improving the half-quadratic splitting algorithm, the image restoration process is divided into two subproblems: deconvolution and denoising. The CNN is able to remove noise in the gradient domain and the learned gradients are then used to guide the image deconvolution step. Compared with several conventional algorithms, scores of evaluation indexes of the proposed method are the highest. When the signal to noise ratio is 40 dB, the average peak signal to noise ratio is raised from 23.7 dB of the degraded images to 30.8 dB of the restored images. The structural similarity index of the results is increased from 0.78 to 0.93. Both quantitative and qualitative evaluations demonstrate that the proposed method is effective.

Object-independent piston diagnosing approach for segmented optical mirrors via deep convolutional neural network.

Piston diagnosing approaches based on neural networks have shown great success, while a few methods are heavily dependent on the imaging target of the optical system. In addition, they are inevitably faced with the interference of submirrors. Therefore, a unique object-independent feature image is used to form an original kind of data set. Besides, an extremely deep image-based convolutional neural network (CNN) of 18 layers is constructed. Furthermore, 9600 images are generated as a data set for each submirror with a special measure of sensitive area extracting. The diversity of results among all the submirrors is also analyzed to ensure generalization ability. Finally, the average root mean square error of six submirrors between the real piston values and the predicted values is approximately 0.0622λ. Our approach has the following characteristics: (1) the data sets are object-independent and contain more effective details, which behave comparatively better in CNN training; (2) the complex network is deep enough and only a limited number of images are required; (3) the method can be applied to the piston diagnosing of segmented mirror to overcome the difficulty brought by the interference of submirrors. Our method does not require special hardware, and is fast to be used at any time, which may be widely applied in piston diagnosing of segmented mirrors.

Breadth-first piston diagnosing approach for segmented mirrors through supervised learning of multiple-wavelength images.

Piston diagnosing approaches for segmented mirrors via machine-learning have shown great success. However, they are inevitably challenged with 2π ambiguity, and the accuracy is usually influenced by the location and number of submirrors. A piston diagnosing approach for segmented mirrors, which employs the breadth-first search (BFS) algorithm and supervised learning strategies of multi-wavelength images, is investigated. An original kind of object-independent and normalized dataset is generated by the in-focal and defocused images at different wavelengths. Additionally, the segmented mirrors are divided into several sub-models of binary tree and are traversed through the BFS algorithm. Furthermore, two deep image-based convolutional neural networks are constructed for predicting the ranges and values of piston aberrations. Finally, simulations are performed, and the accuracy is independent of the location and number of submirrors. The Pearson correlation coefficients for test sets are above 0.99, and the average root mean square error of segmented mirrors is approximately 0.01λ. This technique allows the piston error between segmented mirrors to be measured without 2π ambiguity. Moreover, it can be used for data collected by a real setup. Furthermore, it can be applied to segmented mirrors with different numbers of submirrors based on the sub-model of a binary tree.

Edge detection for optical synthetic apertures based on conditional generative adversarial networks.

Detecting the interference fringes of the optical synthetic aperture is the core in preventing misalignments of the sub-mirrors in piston, tip, and tilt. These fringes are characterized as follows: (1) the edge information of sub-mirrors is accompanied by complex shapes and large gaps; and (2) the traditional edge detection algorithms have different optimal thresholds under different interference fringes, and they may lose boundary information. To address these problems, a novel method for detecting the edge of synthetic aperture fringe images is proposed. Because conditional generative adversarial networks avoid the difficulty of designing the loss function for specific tasks, they are suitable for our project. We trained over 8000 images based on real images and simulated images. Experiments prove that the proposed method can reduce the false detection rate to 0.2, compared with 0.56 by Canny algorithm. This method can also directly detect the fringe edge of the optical synthetic aperture systems, which are accompanied by varied shapes and a growing number of sub-mirrors. When the input images lose boundary information, the traditional algorithm does not restore the boundary, but the proposed method makes a decision globally, and thus it guesses and then fills the boundary. The maximum error of the generated boundary and the actual boundary is two pixels.

Increasing aperture and depth of field simultaneously with wavefront coding technology.

We present a method that simultaneously increases the aperture and depth of field (DOF). A novel, to the best of our knowledge, method called lens-combined wavefront coding (WFC) is proposed for optical design. By rationally balancing rather than minimizing the aberrations, the DOF enhances instead of reduces with expansion of the aperture size. Two optical systems by traditional design and lens-combined WFC are designed to demonstrate our concept. Experiments are conducted using the manufactured lenses to show the extension of the DOF both in the laboratory and the real scene. A spatially invariant deconvolution algorithm is exploited to further suppress the aberrations regarding the field of view. The results show that the aperture and the DOF can be successfully enhanced at the same time.

Rotating asymmetrical phase mask method for improving signal-to-noise ratio in wavefront coding systems.

We present a method that combines an asymmetrical phase mask (PM) and its rotated PM to potentially improve the signal-to-noise ratio in wavefront coding systems. The property of the rotated PM is analyzed. A complementary promotional synthetic optical transfer function is generated as the deconvolution filter for image recovery. The image artifacts are suppressed through processing in the frequency domain. Simulation results provide a quantitative analysis that is based on a cubic PM. Experimental results show that our method can improve image quality over a wide range of defocus.

Analysis of wavefront coding imaging with cubic phase mask decenter and tilt.

This paper has examined how the decenter and tilt of a cubic phase mask plate influence the imaging of a wavefront coding system. The calculated phase term of pupil function with mask decenter and tilt indicates that both decenter and tilt change the shape of the system modulation transfer function in a predictable way by changing the phase and defocus parameters. Simulation in an on-axis three-mirror Cassegrain system is presented to confirm the calculated formula result. Experimental results for mask decenter are also presented. The results demonstrate that the decenter of a phase mask has less effect on the point spread function in the z direction than the x and y directions.

All-optical background subtraction readout method for bimaterial cantilever array sensing.

Optical readout method plays a critical role in bimaterial cantilever array sensing system. The common optical readout methods are based on spectral plane filtering. In the paper an all-optical background subtraction readout approach inspired by total reflection and optical lever principle is presented for the bimaterial cantilever array sensing. Comparing with the spectral plane filtering methods the proposed approach eliminates digital subtraction operation by using optical total reflection instead of digital subtraction and avoids spectral filtering operation. An all-optical background subtraction directly-view infrared sensing system was developed to evaluate the approach. The infrared target can be directly acquired by the visible light CCD. The experimental results and analysis show its unique advantages.

Image quality improvement by the structured light illumination method in an optical readout cantilever array infrared imaging system.

The structured light illumination method is applied in an optical readout uncooled infrared imaging system to improve the IR image quality. The unavoidable nonuniform distribution of the initial bending angles of the bimaterial cantilever pixels in the focal plane array (FPA) can be well compensated by this method. An ordinary projector is used to generate structured lights of different intensity distribution. The projected light is divided into patches of rectangular regions, and the brightness of each region can be set automatically according to the deflection angles of the FPA and the light intensity focused on the imaging plane. By this method, the FPA image on the CCD plane can be much more uniform and the image quality of the IR target improved significantly. A comparative experiment is designed to verify the effectiveness. The theoretical analysis and experimental results show that the proposed structured light illumination method outperforms the conventional one, especially when it is difficult to perfect the FPA fabrication.

High-speed infrared imaging by an uncooled optomechanical focal plane array.

In this paper, we theoretically and experimentally demonstrate that the imaging speed of the optomechanical focal plane array infrared imaging system can be significantly improved by changing the pressure in the vacuum chamber. The decrease in the thermal time constant is attributed to the additional thermal conductance caused by air. The response time will be greatly shortened to about 1/3 time in low vacuum (around ∼10(2) Pa) compared with that in high vacuum. At a chamber pressure of 50 Pa, the "trailing" in the IR image of a moving hot iron is eliminated with negligible deterioration in the image quality. Moreover, infrared images on rapid occurrence events, such as ignition of an alcohol blast burner, lighting and fusion of a tungsten filament, are captured at a frame rate up to 200 Hz. The above results show that the proposed pressure-dependent performance provides a way to improve the system imaging speed and helps to slow down a dynamic event, which is of great value to the uncooled IR imaging systems in practical applications.

Imaging and image restoration of an on-axis three-mirror Cassegrain system with wavefront coding technology.

Wavefront coding (WFC) technology is adopted in the space optical system to resolve the problem of defocus caused by temperature difference or vibration of satellite motion. According to the theory of WFC, we calculate and optimize the phase mask parameter of the cubic phase mask plate, which is used in an on-axis three-mirror Cassegrain (TMC) telescope system. The simulation analysis and the experimental results indicate that the defocused modulation transfer function curves and the corresponding blurred images have a perfect consistency in the range of 10 times the depth of focus (DOF) of the original TMC system. After digital image processing by a Wiener filter, the spatial resolution of the restored images is up to 57.14 line pairs/mm. The results demonstrate that the WFC technology in the TMC system has superior performance in extending the DOF and less sensitivity to defocus, which has great value in resolving the problem of defocus in the space optical system.

Priors-assisted dehazing network with attention supervision and detail preservation.

Single image dehazing is a challenging computer vision task for other high-level applications, e.g., object detection, navigation, and positioning systems. Recently, most existing dehazing methods have followed a "black box" recovery paradigm that obtains the haze-free image from its corresponding hazy input by network learning. Unfortunately, these algorithms ignore the effective utilization of relevant image priors and non-uniform haze distribution problems, causing insufficient or excessive dehazing performance. In addition, they pay little attention to image detail preservation during the dehazing process, thus inevitably producing blurry results. To address the above problems, we propose a novel priors-assisted dehazing network (called PADNet), which fully explores relevant image priors from two new perspectives: attention supervision and detail preservation. For one thing, we leverage the dark channel prior to constrain the attention map generation that denotes the haze pixel position information, thereby better extracting non-uniform feature distributions from hazy images. For another, we find that the residual channel prior of the hazy images contains rich structural information, so it is natural to incorporate it into our dehazing architecture to preserve more structural detail information. Furthermore, since the attention map and dehazed image are simultaneously predicted during the convergence of our model, a self-paced semi-curriculum learning strategy is utilized to alleviate the learning ambiguity. Extensive quantitative and qualitative experiments on several benchmark datasets demonstrate that our PADNet can perform favorably against existing state-of-the-art methods. The code will be available at https://github.com/leandepk/PADNet.