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.

A three-stage deep learning-based training frame for spectra baseline correction.

For spectrometers, baseline drift seriously affects the measurement and quantitative analysis of spectral data. Deep learning has recently emerged as a powerful method for baseline correction. However, the dependence on vast amounts of paired data and the difficulty in obtaining spectral data limit the performance and development of deep learning-based methods. Therefore, we solve these problems from the network architecture and training framework. For the network architecture, a Learned Feature Fusion (LFF) module is designed to improve the performance of U-net, and a three-stage training frame is proposed to train this network. Specifically, the LFF module is designed to adaptively integrate features from different scales, greatly improving the performance of U-net. For the training frame, stage 1 uses airPLS to ameliorate the problem of vast amounts of paired data, stage 2 uses synthetic spectra to further ease reliance on real spectra, and stage 3 uses contrastive learning to reduce the gap between synthesized and real spectra. The experiments show that the proposed method is a powerful tool for baseline correction and possesses potential for application in spectral quantitative analysis.

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.

Multi-Class Wound Classification via High and Low-Frequency Guidance Network.

Wound image classification is a crucial preprocessing step to many intelligent medical systems, e.g., online diagnosis and smart medical. Recently, Convolutional Neural Network (CNN) has been widely applied to the classification of wound images and obtained promising performance to some extent. Unfortunately, it is still challenging to classify multiple wound types due to the complexity and variety of wound images. Existing CNNs usually extract high- and low-frequency features at the same convolutional layer, which inevitably causes information loss and further affects the accuracy of classification. To this end, we propose a novel High and Low-frequency Guidance Network (HLG-Net) for multi-class wound classification. To be specific, HLG-Net contains two branches: High-Frequency Network (HF-Net) and Low-Frequency Network (LF-Net). We employ pre-trained models ResNet and Res2Net as the feature backbone of the HF-Net, which makes the network capture the high-frequency details and texture information of wound images. To extract much low-frequency information, we utilize a Multi-Stream Dilation Convolution Residual Block (MSDCRB) as the backbone of the LF-Net. Moreover, a fusion module is proposed to fully explore informative features at the end of these two separate feature extraction branches, and obtain the final classification result. Extensive experiments demonstrate that HLG-Net can achieve maximum accuracy of 98.00%, 92.11%, and 82.61% in two-class, three-class, and four-class wound image classifications, respectively, which outperforms the previous state-of-the-art methods.

Prediction of Impulsive Aggression Based on Video Images.

In response to the subjectivity, low accuracy, and high concealment of existing attack behavior prediction methods, a video-based impulsive aggression prediction method that integrates physiological parameters and facial expression information is proposed. This method uses imaging equipment to capture video and facial expression information containing the subject's face and uses imaging photoplethysmography (IPPG) technology to obtain the subject's heart rate variability parameters. Meanwhile, the ResNet-34 expression recognition model was constructed to obtain the subject's facial expression information. Based on the random forest classification model, the physiological parameters and facial expression information obtained are used to predict individual impulsive aggression. Finally, an impulsive aggression induction experiment was designed to verify the method. The experimental results show that the accuracy of this method for predicting the presence or absence of impulsive aggression was 89.39%. This method proves the feasibility of applying physiological parameters and facial expression information to predict impulsive aggression. This article has important theoretical and practical value for exploring new impulsive aggression prediction methods. It also has significance in safety monitoring in special and public places such as prisons and rehabilitation centers.

Rational selection of RGB channels for disease classification based on IPPG technology.

The green channel is usually selected as the optimal channel for vital signs monitoring in image photoplethysmography (IPPG) technology. However, some controversies arising from the different penetrability of skin tissue in visible light remain unresolved, i.e., making the optical and physiological information carried by the IPPG signals of the RGB channels inconsistent. This study clarifies that the optimal channels for different diseases are different when IPPG technology is used for disease classification. We further verified this conclusion in the classification model of heart disease and diabetes mellitus based on the random forest classification algorithm. The experimental results indicate that the green channel has a considerably excellent performance in classifying heart disease patients and the healthy with an average Accuracy value of 88.43% and an average F1score value of 93.72%. The optimal channel for classifying diabetes mellitus patients and the healthy is the red channel with an average Accuracy value of 82.12% and the average F1score value of 89.31%. Due to the limited penetration depth of the blue channel into the skin tissue, the blue channel is not as effective as the green and red channels as a disease classification channel. This investigation is of great significance to the development of IPPG technology and its application in disease classification.

Water-Based Coherent Detection of Broadband Terahertz Pulses.

Both solids and gases have been demonstrated as the materials for terahertz (THz) coherent detection. The gas-based coherent detection methods require a high-energy probe laser beam and the detection bandwidth is limited in the solid-based methods. Whether liquids can be used for THz detection and relax these problems has not yet been reported, which becomes a timely and interesting topic due to the recent observation of efficient THz wave generation in liquids. Here, we propose a THz coherent detection scheme based on liquid water. When a THz pulse and a fundamental laser beam are mixed on a free-flowing water film, a second harmonic (SH) beam is generated as the plasma is formed. Combining this THz-induced SH beam with a control SH beam, we successfully achieve the time-resolved waveform of the THz field with the frequency range of 0.1-18 THz. The required probe laser energy is as low as a few microjoules. The sensitivity of our scheme is 1 order of magnitude higher than that of the air-based method under comparable detection conditions. The scheme is sensitive to the THz polarization and the phase difference between the fundamental and control SH beams, which brings direct routes for optimization and polarization sensitive detection. Energy scaling and polarization properties of the THz-induced beam indicate that its generation can be attributed to a four-wave mixing process. This generation mechanism makes simple relationships among the probe laser, THz-induced SH, and THz field, favorable for robustness and flexibility of the detection device.

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.

Molecular dynamic investigation of ethanol-water mixture by terahertz-induced Kerr effect.

The terahertz Kerr effect (TKE) spectroscopy provides time-resolved measurement of low-frequency molecular motions of liquids. Here, the intense broadband terahertz (THz) pulses resonantly excite multiple molecular modes in pure ethanol and ethanol-water mixtures. For pure ethanol, the obtained unipolar TKE response contains the molecular relaxation information extending over tens of picoseconds, which originates from the coupling between the permanent molecular dipole moment of ethanol and the THz electric field. For ethanol-water mixtures with different molar proportions, the results observed on the sub-picosecond time scale can always be divided into the linear superposition of the TKE signals of pure ethanol and water. Under the observation time window over tens of picoseconds (after 1 picosecond), the relative molecular contribution of ethanol in the mixture changes nonlinearly with the increase of water molecules, implying the complex structural perturbation of ethanol hydrogen bond network in the mixture. This work provides a new perspective for further investigation on the hydrogen bond network structure and dynamics in aqueous amphiphilic solutions.

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.

An ultrahigh sensitivity micro-cliff graphene wearable pressure sensor made by instant flash light exposure.

Wearable and highly sensitive pressure sensors are of great importance for robotics, health monitoring and biomedical applications. For simultaneously achieving high sensitivity within a broad working range, fast response time (within a few milliseconds), minimal hysteresis and excellent cycling stability are critical for high performance pressure sensors. However, it remains a major challenge. Herein, we report a conceptual micro-cliff design of a graphene sensor with a record high sensitivity of up to 72 568 kPa-1 in a broad working range of 0-255 kPa, which is one order of magnitude higher than the state-of-the-art reported sensitivity. In addition, the detection limit can be as low as 0.35 Pa and the fast response time is less than 5 ms. The sensor also has a minimal hysteresis and an outstanding cycling stability of 5000 cycles, all of which meet the requirements of an ideal pressure sensor. More interestingly, the micro-cliff graphene sensor is made by the fast and scalable flash reduction of graphene oxide using a single flashlight pulse within 150 ms and has been integrated into a wearable smart insole and an E-glove prototype for demonstration of health monitoring applications. This micro-cliff graphene pressure sensor achieves record-high sensitivity, which brings new possibilities in sensor research and promises broad applications.

Spectral polarization camera based on ghost imaging via sparsity constraints.

A spectral polarization camera based on ghost imaging via sparsity constraints (GISC) is presented. The proposed imager modulates three-dimensional spatial and spectral information of the target into two-dimensional speckle patterns using a spatial random phase modulator and then acquires the speckle patterns at four linear polarization channels through a polarized CCD. The experimental results verify the feasibility of the system structure and reconstruction algorithm. The GISC spectral polarization camera, which has a simple structure and achieves compressive sampling during the imaging acquisition process, provides a simple scheme for obtaining multi-dimensional information of the light field.

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.

Anion-water hydrogen bond vibration revealed by the terahertz Kerr effect.

The microscopic mechanism for ionic influence on the hydrogen bond network of water has not been fully understood. Here we employ the terahertz Kerr effect (TKE) technique to map the intermolecular hydrogen bond dynamics in a series of aqueous halide solutions at the sub-picosecond scale. Compared with pure water, the significantly enhanced bipolar TKE response associated with polarization anisotropy in an ionic aqueous solution is successfully captured. We decompose the measured TKE response into different molecular motion modes and demonstrate that the obviously increasing positive polarity response is mainly due to the anion-water hydrogen bond vibration mode with the resonant THz electric field excitation. Our measurement results provide an experimental basis for further insight into the effects of ions on the structure and dynamics of a hydrogen bond in water.

Reconfigurable optical time delay array for 3D lidar scene projector.

3D lidar scene projector (LSP) plays an important role in the hardware-in-the-loop (HIL) simulation for autonomous driving system (ADS). It generates a simulated 3D lidar scene in laboratory by generating a 2D array of optical time delay signals. The reconfigurable optical time delay array (ROTDA) is crucial for LSP. However, current ROTDA solutions cannot support a LSP with a spatial resolution more than 10×10. In this paper, we proposed a novel ROTDA design based on the time slicing method. The optical signals with the same time delay but different spatial coordinates were treated as one time slice. Different time slices were superimposed into a composite image by a microlens-array-based imaging system to obtain a 3D lidar scene. And a spatial light modulator (SLM) was utilized to configure the time delay of each lidar scene pixel. We developed a ROTDA prototype with 64×64 pixels, each pixel can be reconfigured with up to 180 different time delays in one frame. The time delay resolution is 1 ns, the maximum time delay is 5000 s, and the 3D frame rate is 20Hz. The prototype can generate a continuous lidar scene with a distance span of 27 m, and can also generate up to 8 short scenes that are separated from each other along the lidar observation direction, each short scene covers a distance span of 3 m or 3.75 m. The design method proposed in this paper can also be applied to other occasions that demand a large number of time delay generators.

Ultrafast optical pulse polarization modulation based on the terahertz-induced Kerr effect in low-density polyethylene.

Controlling the polarization state of an optical pulse within a short gating time facilitates ultrafast all-optical data processing and recording. Using the innovative all-optical modulation method such as the transient terahertz Kerr effect (TKE), the polarization state of the optical pulse can be switched within the gating time on the sub-picosecond scale. In this work, we use high-frequency single-cycle terahertz (THz) pulses to excite the Kerr effects of materials and explore the potential to shorten the gating time of the polarization modulator. A low-density polyethylene (LDPE) material with good Kerr-related properties is proposed to improve the performance of the TKE-based modulator and the obtained ultrafast gating time (FWHM) can reach 86 fs. Experimental evidence for the thickness dependence of the Kerr response demonstrates that the errors caused by optical transmission factors in the LDPE medium can be ignored, and thus the ultrafast gating modulation is mainly limited by the duration of probe pulse. Compared with common TKE-based materials, we believe that the low-cost LDPE is a good candidate to achieve high-power TKE-based ultrafast pulse switching.

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.

Ultrafast hydrogen bond dynamics of liquid water revealed by terahertz-induced transient birefringence.

The fundamental properties of water molecules, such as their molecular polarizability, have not yet been clarified. The hydrogen bond network is generally considered to play an important role in the thermodynamic properties of water. The terahertz (THz) Kerr effect technique, as a novel tool, is expected to be useful in exploring the low-frequency molecular dynamics of liquid water. Here, we use an intense and ultrabroadband THz pulse (peak electric field strength of 14.9 MV/cm, centre frequency of 3.9 THz, and bandwidth of 1-10 THz) to resonantly excite intermolecular modes of liquid water. Bipolar THz field-induced transient birefringence signals are observed in a free-flowing water film. We propose a hydrogen bond harmonic oscillator model associated with the dielectric susceptibility and combine it with the Lorentz dynamic equation to investigate the intermolecular structure and dynamics of liquid water. We mainly decompose the bipolar signals into a positive signal caused by hydrogen bond stretching vibration and a negative signal caused by hydrogen bond bending vibration, indicating that the polarizability perturbation of water presents competing contributions under bending and stretching conditions. A Kerr coefficient equation related to the intermolecular modes of water is established. The ultrafast intermolecular hydrogen bond dynamics of water revealed by an ultrabroadband THz pump pulse can provide further insights into the transient structure of liquid water corresponding to the pertinent modes.

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.

A camera-based ballistocardiogram heart rate measurement method.

Recent studies have shown that head movements associated with cardiac activity contain a heart rate (HR) signal. In most previous studies, subjects were required to remain stationary in a specific environment during HR measurements, and measurement accuracy depended on the choice of target in the scene, i.e., the specified region of the face. In this paper, we proposed a robust HR measurement method based on ballistocardiogram (BCG) technology. This method requires only a camera and does not require that users establish a complex measurement environment. In addition, a bidirectional optical flow algorithm is designed to select and track valid feature points in the video captured by using the camera. Experiments with 11 subjects show that the HR values measured using the proposed method differ slightly from the reference values, and the average error is only 1.09%. Overall, this method can improve the accuracy of BCG without limitations related to skin tone, illumination, the state of the subject, or the test location.