Fast surface signal extraction method for photon point clouds with strong background noise without prior altitude information.

It is extremely challenging to rapidly and accurately extract target echo photon signals from massive photon point clouds with strong background noise without any prior geographic information. Herein, we propose a fast surface detection method realized by combining the improved density-dimension algorithm (DDA) and Kalman filtering (KF), termed the DDA-KF algorithm, for photon signals with a high background noise rate (BNR) to improve the extraction of surface photon signals from spacecraft platforms. The results showed that the algorithm exhibited good adaptability to strong background noise and terrain slope variations, and had real-time processing capabilities for massive photon point clouds in large-scale detection range without prior altitude information of target. Our research provides a practical technical solution for single-photon lidar applications in deep space navigation and can help improve the performance in environments characterized by strong background noise.

Pol2Pol: self-supervised polarimetric image denoising.

In this Letter, we present a self-supervised method, polarization to polarization (Pol2Pol), for polarimetric image denoising with only one-shot noisy images. First, a polarization generator is proposed to generate training image pairs, which are synthesized from one-shot noisy images by exploiting polarization relationships. Second, the Pol2Pol method is extensible and compatible, and any network that performs well in supervised image denoising tasks can be deployed to Pol2Pol after proper modifications. Experimental results show Pol2Pol outperforms other self-supervised methods and achieves comparable performance to supervised methods.

Polarized image super-resolution via a deep convolutional neural network.

Reduced resolution of polarized images makes it difficult to distinguish detailed polarization information and limits the ability to identify small targets and weak signals. A possible way to handle this problem is the polarization super-resolution (SR), which aims to obtain a high-resolution polarized image from a low-resolution one. However, compared with the traditional intensity-mode image SR, the polarization SR is more challenging because more channels and their nonlinear cross-links need to be considered as well as the polarization and intensity information need to be reconstructed simultaneously. This paper analyzes the polarized image degradation and proposes a deep convolutional neural network for polarization SR reconstruction based on two degradation models. The network structure and the well-designed loss function have been verified to effectively balance the restoration of intensity and polarization information, and can realize the SR with a maximum scaling factor of four. Experimental results show that the proposed method outperforms other SR methods in terms of both quantitative evaluation and visual effect evaluation for two degradation models with different scaling factors.

Physics-informed neural network for polarimetric underwater imaging.

Utilizing the polarization analysis in underwater imaging can effectively suppress the scattered light and help to restore target signals in turbid water. Neural network-based solutions can also boost the performance of polarimetric underwater imaging, while most of the existing networks are pure data driven which suffer from ignoring the physical mode. In this paper, we proposed an effective solution that informed the polarimetric physical model and constrains into the well-designed deep neural network. Especially compared with the conventional underwater imaging model, we mathematically transformed the two polarization-dependent parameters to a single parameter, making it easier for the network to converge to a better level. In addition, a polarization perceptual loss is designed and applied to the network to make full use of polarization information on the feature level rather than on the pixel level. Accordingly, the network was able to learn the polarization modulated parameter and to obtain clear de-scattered images. The experimental results verified that the combination of polarization model and neural network was beneficial to improve the image quality and outperformed other existing methods, even in a high turbidity condition.

Real-time distance and velocity measurement based on the dual-comb system.

With the development of laser metrology, the dual-comb system has natural superiority in the measuring fields. Specifically, distance and velocity represent a basic state for the target in space. We propose an application mode of the dual-comb interferometry integrated into the field programmable gate array. A high-speed parallel processor truly gives full play to the benefit of the data processing rate. The algorithm of the peak extraction and the address matching also bring an efficient working mode into the whole scheme. To verify the performance of this system, we devise a series of experiments for distance and velocity, respectively. The data processing rate of the distance is 425 Hz and that of the corresponding average velocity is 0.425 Hz, which is flexible for different measuring conditions. The experimental results show that the difference can be well within 252.8 µm at 5 m range and 284.9 µm/s over 0.5 m/s.

Attention-based neural network for polarimetric image denoising.

In this Letter, we propose an attention-based neural network specially designed for the challenging task of polarimetric image denoising. In particular, the channel attention mechanism is used to effectively extract the features underlying the polarimetric images by rescaling the contributions of channels in the network. In addition, we also design the adaptive polarization loss to make the network focus on the polarization information. Experiments show that our method can well restore the details flooded by serious noise and outperforms previous methods. Moreover, the underlying mechanism of channel attention is revealed visually.

Three-Dimensional Imaging by Frequency-Comb Spectral Interferometry.

In this paper, we demonstrate a three-dimensional imaging system based on the laser frequency comb. We develop a compact, all-fiber mode-locked laser at 1 µm, whose repetition frequency can be tightly synchronized to the external frequency reference. The mode-locked state is achieved via the saturable absorber mirror in a linear cavity, and the laser output power can be amplified from 4 mW to 150 mW after a Yb-doped fiber amplifier. Three-dimensional imaging is realized via the spectral interferometry with the aid of an equal-arm Michelson interferometer. Compared with the reference values, the measurement results show the difference can be below 4 µm. Our system could provide a pathway to the real industry applications in future.

Long distance measurement by dynamic optical frequency comb.

In this paper, we propose a method aiming to measure the absolute distance via the slope of the inter-mode beat phase by sweeping the repetition frequency of the frequency comb. The presented approach breaks the inertial thinking of the extremely stable comb spacing, and the bulky phase-locking circuit of the repetition frequency is not required. In particular, the non-ambiguity range can be expanded to be infinite. To verify the performance of presented method, a series of distance experiments have been devised in different scenarios. Compared with the reference values, the experimental results show the differences within 25 µm at 65 m range in the laboratory, and within 100 µm at 219 m range out of the lab.

Effects of non-sinusoidal pitching motion on the propulsion performance of an oscillating foil.

Numerical simulations have been used in this paper to study the propulsion device of a wave glider based on an oscillating hydrofoil, in which the profile of the pitching and heaving motion have been prescribed for the sake of simplicity. A grid model for a two-dimensional NACA0012 hydrofoil was built by using the dynamic and moving mesh technology of the Computational Fluid Dynamics (CFD) software FLUENT and the corresponding mathematical model has also been established. First, for the sinusoidal pitching, the effects of the pitching amplitude and the reduced frequency were investigated. As the reduced frequency increased, both the mean output power coefficient and the optimal pitching amplitude increased. Then non-sinusoidal pitching was studied, with a gradual change from a sinusoid to a square wave as the value of ß was increased from 1. It was found that when the pitching amplitude was small, the trapezoidal pitching profile could indeed improve the mean output power coefficient of the flapping foil. However, when the pitching amplitude was larger than the optimal value, the non-sinusoidal pitching motion negatively contributed to the propulsion performance. Finally, the overall results suggested that a trapezoidal-like pitching profile was effective for the oscillating foil of a wave glider when the pitching amplitude was less than the optimal value.

Characterization of pulsed ultrasound using optical detection in Raman-Nath regime.

In this paper, we describe an optical detection method for the characterization of pulsed ultrasound based on acousto-optic interaction. We deduce the relationship between the ultrasound and the diffracted light from the principle of acousto-optic diffraction in the Raman-Nath regime, which is verified experimentally. Five ultrasonic transducers with different central frequencies and different focusing types are measured to show the method's performance regarding linearity, sound pressure measurement, phase measurement, frequency response, and spatial resolution. The experimental results show a good agreement with simulation data by CIVA (ultrasonic simulation software, M2M NDT, Inc.) and the pulse-echo method.

Direct measurement of the sound velocity in seawater based on the pulsed acousto-optic effect between the frequency comb and the ultrasonic pulse.

We present a new method to measure the velocity of sound in pure water and seawater using the Raman-Nath diffraction caused by acousto-optic effect between the optical frequency comb and the ultrasonic pulse. In the Mach-Zehnder interferometry system we established, the measurement and reference arms are tagged with sharp negative pulses caused by the pulsed ultrasound passing through them. The difference in optical path between the two parallel beams is twice the flight distance of the ultrasonic waves. The span between the two negative pulses reflects the time interval. At the same time, the distance between the two arms can be measured precisely using the femtosecond laser interferometry. Consequently, the time interval and the distance can be used to measure the sound velocity. The experimental results show that, the uncertainty of the sound speed measurement can achieve 0.03m/s@1482m/s in pure water and 0.029m/s@1527m/s in seawater, respectively, compared with the commercial sound velocity profiler (SVP). More importantly, benefiting from the faster and cleaner response of the acousto-optic effect than the piezoelectric effect which is widely adopted in direct sound velocity measurement method, our method provides a new idea for the metrology of sound velocity in seawater.