Short-wavelength-infrared upconversion edge enhancement imaging based on a Laguerre-Gaussian composite vortex filter.

Edge-enhanced imaging by spiral phase contrast has proven instrumental in revealing phase or amplitude gradients of an object, with notable applications spanning feature extraction, target recognition, and biomedical fields. However, systems deploying spiral phase plates encounter limitations in phase mask modulation, hindering the characterization of the modulation function during image reconstruction. To address this need, we propose and demonstrate an innovative nonlinear reconstruction method using a Laguerre-Gaussian composite vortex filter, which modulates the spectrum of the target. The involved nonlinear process spectrally transforms the incident short-wavelength-infrared (SWIR) signal from 1550 to 864 nm, subsequently captured by a silicon charge-coupled device. Compared with conventional schemes, our novel filtering method effectively suppresses the diffraction noise, significantly enhancing image contrast and resolution. By loading specific phase holograms on the spatial light modulator, bright-field imaging, isotropic, amplitude-controlled anisotropic, and directional second-order edge-enhanced imaging are realized. Anticipated applications for the proposed SWIR edge-enhanced imaging system encompass domains such as artificial intelligence recognition, deep tissue medical diagnostics, and non-destructive defect inspection. These applications underscore the valuable potential of our cutting-edge methodology in furthering both scientific exploration and practical implementations.

Real-infraredSR: real-world infrared image super-resolution via thermal imager.

Infrared image super-resolution technology aims to overcome the pixel size limitation of the infrared focal plane array for higher resolution images. Due to the real-world images with different resolutions having more complex degradation processes than mathematical calculation, most existing super-resolution methods using the synthetic data obtained by bicubic interpolation achieve unsatisfactory reconstruction performance in real-world scenes. To solve this, this paper innovatively proposes an infrared real-world dataset with different resolutions based on a refrigerated thermal detector and the infrared zoom lens, enabling the network to acquire more realistic details. We obtain images under different fields of view by adjusting the infrared zoom lens and then achieve the scale and luminance alignment of high and low-resolution (HR-LR) images. This dataset can be used for infrared image super-resolution, with an up-sampling scale of two. In order to learn complex features of infrared images efficiently, an asymmetric residual block structure is proposed to effectively reduce the number of parameters and improve the performance of the network. Finally, to solve the slight misalignment problem in the pre-processing stage, contextual loss and perceptual loss are introduced to improve the visual performance. Experiments show that our method achieves superior results both in reconstruction effect and practical value for single infrared image super-resolution in real scenarios.

Insights into the mechanism of persulfate activation with carbonated waste metal adsorbed resin for the degradation of 2,4-dichlorophenol.

Superabsorbent resin (SAR) saturated with heavy metals poses a threat to surrounding ecosystem. To promote the reutilization of waste, resins adsorbed by Fe2+ and Cu2+ were carbonized and used as catalysts (Fe@C/Cu@C) to activate persulfate (PS) for 2,4-dichlorophenol (2,4-DCP) degradation. The heterogeneous catalytic reaction was mainly responsible for 2,4-DCP removal. The synergistic effect of Fe@C and Cu@C was propitious to 2,4-DCP degradation. Fe@C/Cu@C with a ratio of 2:1 showed the highest performance of 2,4-DCP removal. 40 mg/L 2,4-DCP was completely removed within 90 min under reaction conditions of 5 mM PS, pH = 7.0 and T = 25 °C. The cooperation of Fe@C and Cu@C facilitated the redox cycling of Fe and Cu species to supply accessible PS activation sites, enhancing ROS generation for 2,4-DCP degradation. Carbon skeleton enhanced 2,4-DCP removal via radical/nonradical oxidation pathways and via its adsorption to 2,4-DCP. SO4˙-, HO˙ and O2•- were the dominate radical species involved in 2,4-DCP destruction. Meanwhile, the possible pathways of 2,4-DCP degradation were proposed based on GC-MS. Finally, recycling tests proved catalysts exhibited recyclable stability. Aiming to resource utilization, Fe@C/Cu@C with satisfactory catalysis and stability, is promising catalyst for contaminated water treatment.

Hybrid spatial-spectral generative adversarial network for hyperspectral image classification.

In recent years, generative adversarial networks (GNAs), consisting of two competing 2D convolutional neural networks (CNNs) that are used as a generator and a discriminator, have shown their promising capabilities in hyperspectral image (HSI) classification tasks. Essentially, the performance of HSI classification lies in the feature extraction ability of both spectral and spatial information. The 3D CNN has excellent advantages in simultaneously mining the above two types of features but has rarely been used due to its high computational complexity. This paper proposes a hybrid spatial-spectral generative adversarial network (HSSGAN) for effective HSI classification. The hybrid CNN structure is developed for the construction of the generator and the discriminator. For the discriminator, the 3D CNN is utilized to extract the multi-band spatial-spectral feature, and then we use the 2D CNN to further represent the spatial information. To reduce the accuracy loss caused by information redundancy, a channel and spatial attention mechanism (CSAM) is specially designed. To be specific, a channel attention mechanism is exploited to enhance the discriminative spectral features. Furthermore, the spatial self-attention mechanism is developed to learn the long-term spatial similarity, which can effectively suppress invalid spatial features. Both quantitative and qualitative experiments implemented on four widely used hyperspectral datasets show that the proposed HSSGAN has a satisfactory classification effect compared to conventional methods, especially with few training samples.

Enhancing infrared imaging systems with temperature-dependent nonuniformity correction via single-frame and inter-frame structural similarity.

Temperature-dependent nonuniformity in infrared images significantly impacts image quality, necessitating effective solutions for intensity nonuniformity. Existing variational models primarily rely on gradient prior constraints from single-frame images, resulting in limitations due to insufficient exploitation of intensity characteristics in both single-frame and inter-frame images. This paper introduces what we believe to be a novel variational model for nonuniformity correction (NUC) that leverages single-frame and inter-frame structural similarity (SISB). This approach capitalizes on the structural similarities between the corrected image, intensity bias map, and degraded image, facilitating efficient suppression of intensity nonuniformity in real-world scenarios. The proposed method diverges fundamentally from existing strategies and demonstrates superior performance in comparison with state-of-the-art correction models.

Active non-uniform illumination-based underwater polarization imaging method for objects with complex polarization properties.

Active polarization imaging is one of the most effective underwater optical imaging methods that can eliminate the degradation of image contrast and clarity caused by macro-molecule scattering. However, the non-uniformity of active illumination and the diversity of object polarization properties may decrease the quality of underwater imaging. This paper proposes a non-uniform illumination-based active polarization imaging method for underwater objects with complex optical properties. Firstly, illumination homogenization in the frequency domain is proposed to extract and homogenize the natural incident light from the total receiving light. Then, the weight values of the polarized and non-polarized images are computed according to each pixel's degree of linear polarization (DoLP) in the original underwater image. By this means, the two images can be fused to overcome the problem of reflected light loss generated by the complex polarization properties of underwater objects. Finally, the fusion image is normalized as the final result of the proposed underwater polarization imaging method. Both qualitative and quantitative experimental results show that the presented method can effectively eliminate the uneven brightness of the whole image and obtain the underwater fusion image with significantly improved contrast and clarity. In addition, the ablation experiment of different operation combinations shows that each component of the proposed method has noticeable enhancement effects on underwater polarization imaging. Our codes are available in Code 1.

Unsupervised end-to-end infrared and visible image fusion network using learnable fusion strategy.

Infrared and visible image fusion aims to reconstruct fused images with comprehensive visual information by merging the complementary features of source images captured by different imaging sensors. This technology has been widely used in civil and military fields, such as urban security monitoring, remote sensing measurement, and battlefield reconnaissance. However, the existing methods still suffer from the preset fusion strategies that cannot be adjustable to different fusion demands and the loss of information during the feature propagation process, thereby leading to the poor generalization ability and limited fusion performance. Therefore, we propose an unsupervised end-to-end network with learnable fusion strategy for infrared and visible image fusion in this paper. The presented network mainly consists of three parts, including the feature extraction module, the fusion strategy module, and the image reconstruction module. First, in order to preserve more information during the process of feature propagation, dense connections and residual connections are applied to the feature extraction module and the image reconstruction module, respectively. Second, a new convolutional neural network is designed to adaptively learn the fusion strategy, which is able to enhance the generalization ability of our algorithm. Third, due to the lack of ground truth in fusion tasks, a loss function that consists of saliency loss and detail loss is exploited to guide the training direction and balance the retention of different types of information. Finally, the experimental results verify that the proposed algorithm delivers competitive performance when compared with several state-of-the-art algorithms in terms of both subjective and objective evaluations. Our codes are available at https://github.com/MinjieWan/Unsupervised-end-to-end-infrared-and-visible-image-fusion-network-using-learnable-fusion-strategy.

Frequency upconversion imaging based on Hadamard coding.

A frequency upconversion imaging based on Hadamard coding is presented to remove the distorting effect on condition that the pump beam is tightly focused to optimize the conversion efficiency. The distortion caused by the convolution between the object field and the pump field is ascribed to the point spread function effect. In order to remove the blurring in an upconversion imaging system optimized by tight focused pump, the object is encoded by measurement matrices and the corresponding intensity of the converted field is measured. Thus the intensity distribution of the object can be calculated accurately by the measurements and the measurement matrix. The signal-to-noise ratio (SNR) is improved by employing the Hadamard matrix since the intensity of measured converted signal is far larger than the intensity of each pixel. The experimental results show the proposed method removes the distorting effect caused by the convolution. The converted image still has sharp edges on condition that the conversion efficiency is optimized by tight focusing the pump beam.

Bandwidth extension method based on the field-shunting effect in a high-gain photoelectric receiver circuit.

In the high-gain photoelectric receiver circuit, the method based on the field-shunting effect is applied to improve the bandwidth of the transimpedance amplifier. This method is implemented by adding a ground trace under the gain resistor, which reduces the parasitic capacitance of the gain resistor and thus increases the bandwidth. To obtain the specific impact of this method on bandwidth, a series of simulations are carried out, including electromagnetic simulations of a three-dimensional structure of circuit gain part and simulation program with integrated circuit emphasis (SPICE) simulations of the high-gain voltage-current feedback transimpedance amplifier. Finally, the optimal simulation result shows that selecting a 1206 size chip fixed resistor and setting the ground trace width to 1.1 mm can greatly reduce the influence of resistor parasitic effects on the circuit, thereby achieving the best performance of bandwidth extension. Further, the comparative experiment also verifies the effectiveness of the method for bandwidth enhancement.

Pulsed light time-of-flight measurement based on a differential hysteresis timing discrimination method.

In the pulsed light time-of-flight (ToF) measurement, the timing point generated in the receiver channel is very important to the measurement accuracy. Therefore, a differential hysteresis timing discrimination method is proposed to generate timing points of the receiver channel. This method is based on utilizing the unbalanced characteristics of the fully differential operational amplifier circuit as well as introducing extra hysteresis levels to achieve the stable generation of timing points. With this method, fewer circuit components are consumed and the dynamic range of the receiver channel is not limited by its linear range. The experiments demonstrate that a receiver channel applying the proposed discrimination reaches better single shot accuracy compared to that using leading-edge timing discrimination. This method is also suitable for the timing walk error compensation by means of pulse width. Finally, these results verify the effectiveness of the proposed method in pulsed light ToF measurement.

Adaptive compressed 3D ghost imaging based on the variation of surface normals.

Three-dimensional (3D) imaging can be reconstructed by a computational ghost imaging system with single pixel detectors based on a photometric stereo, but the requirement of large measurements and long imaging times are obstacles to its development. Also, the compressibility of the target's surface normals has not been fully studied, which causes the waste in sampling efficiency in single-pixel imaging. In this paper, we propose a method to adaptively measure the object's 3D information based on surface normals. In the proposed method, the regions of object's surface are illuminated by patterns of different spatial resolutions according to the variation of surface normals. The experimental results demonstrate that our proposed scheme can reduce measurements and preserve the quality of the formed 3D image.

Calculation method of multiangle polarization measurement for oil spill detection.

This paper takes thin oil film on the sea surface in oil spill pollution as the subject of our research. Combined with physical characteristics of the target, the Mueller matrix of the target is analyzed via a polarization technique. The paper uses the Mueller matrix derived from the Mueller-Jones matrix. Based on the Mueller-Jones matrix, the optical recognition model of oil film on the sea surface is established. This paper proposes a multiangle polarization measurement technology for oil spill pollution on the sea surface and proposes a new data processing method. By calculating the corresponding amplitude ratio, phase retardation, refractive index, and degree of polarization of each pixel, the optical properties of the oil film on the sea surface are analyzed; the surface characteristics of oil film on the sea surface are extracted; the calculation accuracy is improved; and ocean oil spill optical recognition is developed.

Depth imaging denoising of photon-counting lidar.

Photon-counting lidar systems have difficulty reconstructing target depth images due to ambient noise. In this paper, we propose a novel way of using correlative photons and spatial correlations to reduce the false alarm probability. Experimental results show that the root mean square error of the depth image reconstructed by the proposed algorithm can be 1.68 times and 1.11 times better than that of the fast depth imaging denoising algorithm and log-matched filter estimation. The experimental results show that the proposed algorithm can effectively improve the reconstructed image of photon-counting lidar.

Calculating the model of a nondiagonal rotation invariant angle for a complicated scatterer.

In the research on scattering polarimetry, a scattering mechanism is described by an internal degree of freedom called rotation invariant parameter α. Traditionally, α is calculated by the diagonal scattering matrix of the single scatterer. However, when the research object is a scatterer with complicated surface and microstructure, the traditional calculation of parameter α is biased, since the corresponding scattering matrix is a nondiagonal matrix. To address this problem, this paper proposes a scientific model based on Cameron decomposition to raise the accuracy of parameter α in the complicated scatterer. The rotation invariant parameter with higher accuracy is renamed as a nondiagonal rotation invariant angle. In the verified experiments, the experimental values of each nondiagonal rotation invariant angle are compared with the referenced values calculated by optical constant and incident angle. The results demonstrate that fewer residuals are achieved than by the traditional method. Based on the presented calculating model, the scattering mechanism difference interval between two different materials is proposed as a judging area to distinguish the differences between scattering mechanisms in application.

Improved algorithm for expanding the measurement linear range of a four-quadrant detector.

A four-quadrant detector is a kind of photoelectric detector that can quickly and accurately measure the incident angle of light. However, its ability to measure in a large field of view (FOV) is limited by its hardware structure and its calculation principle. To solve these problems, this paper proposes an improved algorithm that can extend the measurement linear range without reducing its measurement accuracy. After that, through simulation and experiment, we compare it with many other location algorithms, including the most widely used classical algorithm and the logarithmic algorithm suitable for large FOVs. Finally, the following conclusions can be drawn from both theoretical data and experimental data: the improved algorithm can significantly improve the measurement accuracy over 50% in the same FOV condition, and the measurable range can be expanded over 25% in the same accuracy requirement. At the same time, the robustness of noise does not decrease; when the root mean square error of the classical algorithm fluctuates at 0.1° in different SNR conditions, the improved algorithm is also 0.1°, while the logarithmic algorithm can reach 1.7°, and other algorithms are around 0.25°. In addition, the improved algorithm is more stable in measuring a certain direction and can effectively avoid the influence from the offset of incident light in another axis.

Object Tracking Based on Vector Convolutional Network and Discriminant Correlation Filters.

Due to the fast speed and high efficiency, discriminant correlation filter (DCF) has drawn great attention in online object tracking recently. However, with the improvement of performance, the costs are the increase in parameters and the decline of speed. In this paper, we propose a novel visual tracking algorithm, namely VDCFNet, and combine DCF with a vector convolutional network (VCNN). We replace one traditional convolutional filter with two novel vector convolutional filters in the convolutional stage of our network. This enables our model with few memories (only 59 KB) trained offline to learn the generic image features. In the online tracking stage, we propose a coarse-to-fine search strategy to solve drift problems under fast motion. Besides, we update model selectively to speed up and increase robustness. The experiments on OTB benchmarks demonstrate that our proposed VDCFNet can achieve a competitive performance while running over real-time speed.

Automatic Lumbar MRI Detection and Identification Based on Deep Learning.

The aim of this research is to automatically detect lumbar vertebras in MRI images with bounding boxes and their classes, which can assist clinicians with diagnoses based on large amounts of MRI slices. Vertebras are highly semblable in appearance, leading to a challenging automatic recognition. A novel detection algorithm is proposed in this paper based on deep learning. We apply a similarity function to train the convolutional network for lumbar spine detection. Instead of distinguishing vertebras using annotated lumbar images, our method compares similarities between vertebras using a beforehand lumbar image. In the convolutional neural network, a contrast object will not update during frames, which allows a fast speed and saves memory. Due to its distinctive shape, S1 is firstly detected and a rough region around it is extracted for searching for L1-L5. The results are evaluated with accuracy, precision, mean, and standard deviation (STD). Finally, our detection algorithm achieves the accuracy of 98.6% and the precision of 98.9%. Most failed results are involved with wrong S1 locations or missed L5. The study demonstrates that a lumbar detection network supported by deep learning can be trained successfully without annotated MRI images. It can be believed that our detection method will assist clinicians to raise working efficiency.

Bioleaching of two different genetic types of chalcopyrite and their comparative mineralogical assessment.

The bioleaching of two different genetic types of chalcopyrite by the moderate thermophile Sulfobacillus thermosulfidooxidans was investigated by leaching behaviors elucidation and their comparative mineralogical assessment. The leaching experiment showed that the skarn-type chalcopyrite (STC) revealed a much faster leaching rate with 33.34% copper extracted finally, while only 23.53% copper was bioleached for the porphyry-type chalcopyrite (PTC). The mineralogical properties were analyzed by XRD, SEM, XPS, and Fermi energy calculation. XRD indicated that the unit cell volume of STC was a little larger than that of PTC. SEM indicated that the surface of STC had more steps and ridges. XPS spectra showed that Cu(I) was the dominant species of copper on the surfaces of the two chalcopyrite samples, and STC had much more copper with lower Cu 2p3/2 binding energy. Additionally, the Fermi energy of STC was much higher than that of PTC. These mineralogical differences were in good agreement with the bioleaching behaviors of chalcopyrite. This study will provide some new information for evaluating the oxidation kinetics of chalcopyrite.

Quadrant response model and error analysis of four-quadrant detectors related to the non-uniform spot and blind area.

In the system of tracking and detection based on the four-quadrant detector (4-QD), the energy distribution of the incident spot and the blind area of the photosensitive surface will affect the location accuracy. The current model of the spot is based on the ideal circular Gauss spot, which makes the error caused by the spot shape easily ignored. In this paper, the model of the spot energy distribution is improved, which can adapt to the elliptical Gauss distribution. The width of the blind area is also added to the response models of the detector so that the output of each quadrant and the error of the localization algorithm can be calculated more accurately. The simulation results show that the measurement accuracy of 4-QD decreases with the increase of the blind area, the shape, and the inclination of the light spot. In the experiment, we first verify the correctness and practicability of the improved model of the spot energy distribution, and then the improved model is proved to be able to make the response and error calculation more accurate.

Inclinometer Assembly Error Calibration and Horizontal Image Correction in Photoelectric Measurement Systems.

Inclinometer assembly error is one of the key factors affecting the measurement accuracy of photoelectric measurement systems. In order to solve the problem of the lack of complete attitude information in the measurement system, this paper proposes a new inclinometer assembly error calibration and horizontal image correction method utilizing plumb lines in the scenario. Based on the principle that the plumb line in the scenario should be a vertical line on the image plane when the camera is placed horizontally in the photoelectric system, the direction cosine matrix between the geodetic coordinate system and the inclinometer coordinate system is calculated firstly by three-dimensional coordinate transformation. Then, the homography matrix required for horizontal image correction is obtained, along with the constraint equation satisfying the inclinometer-camera system requirements. Finally, the assembly error of the inclinometer is calibrated by the optimization function. Experimental results show that the inclinometer assembly error can be calibrated only by using the inclination angle information in conjunction with plumb lines in the scenario. Perturbation simulation and practical experiments using MATLAB indicate the feasibility of the proposed method. The inclined image can be horizontally corrected by the homography matrix obtained during the calculation of the inclinometer assembly error, as well.