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.

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.

Heap leaching of ion adsorption rare earth ores and REEs recovery from leachate with lixiviant regeneration.

In this work, semi-industrial scale heap leaching of 200 t ion adsorption rare earth ores (IRE-ore) and rare earth elements (REEs) recovery from lixivium was first conducted. Biosynthetic citrate/(Na)3Cit, a typical microbial metabolite, was chosen as the lixiviant to conduct heap leaching. Subsequently, an organic precipitation method was proposed, which used oxalic acid to effectively recover REEs and reduce the production cost by lixiviant regeneration. The results showed that the heap leaching efficiency of REEs reached 98 % with a lixiviant concentration of 50 mmol/L and a solid-liquid ratio of 1:2. The lixiviant can be regenerated during the precipitation process, with REE yields and impurity aluminum yields of 94.5 % and 7.4 %, respectively. The residual solution can then be cyclically used as a new lixiviant after simple adjustment. High-quality rare earth concentrates with a rare earth oxide (REO) content of 96 % can be finally obtained after roasting. This work provides an eco-friendly alternative for IRE-ore extraction to solve the environmental issues caused by traditional technology. The results proved feasibility and provided a foundation for in situ (bio)leaching processes in further industrial tests and production.

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.

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.

Effective recovery of rare earth from (bio)leaching solution through precipitation of rare earth-citrate complex.

Bioleaching is considered an alternative to traditional rare earth extraction technology. However, since rare earth elements exist as complexes in bioleaching lixivium, they cannot be directly precipitated by normal precipitants, which restricts their further development. This structurally stable complex is also a common challenge in various types of industrial wastewater treatment. In this work, a new method called a three-step precipitation process is first proposed to efficiently recover rare earth-citrate (RE-Cit) complexes from (bio)leaching lixivium. It consists of coordinate bond activation (carboxylation by pH adjustment), structure transformation (Ca2+ addition) and carbonate precipitation (soluble CO32- addition). The optimization conditions are determined to adjust the lixivium pH to around 2.0, then add calcium carbonate until the n(Ca2+): n(Cit3-) is more than 1.4:1 and lastly add sodium carbonate until n(CO32-): n(RE3+) is more than 4:1. The results of precipitation experiments using imitated lixivium show that the rare earth yield is more than 96% and the impurity aluminum yield is less than 20%. Subsequently, pilot tests (1000 L) using real lixivium were successfully conducted. The precipitation mechanism is briefly discussed and proposed by thermogravimetric analysis, Fourier infrared spectroscopy, Raman spectroscopy and UV spectroscopy. This technology is promising in the industrial application of rare earth (bio)hydrometallurgy and wastewater treatment due to its advantages of high efficiency, low cost, environmental friendliness and simple operation.

ROS formation driven by pyrite-mediated arsenopyrite oxidation and its potential role on arsenic transformation.

Pyrite-mediated arsenopyrite oxidation is an important process affecting arsenic (As) mobility. The iron sulfides-induced reactive oxidation species (ROS) can exert significant influence on As transformation. However, the impact of pyrite-arsenopyrite association on ROS production and its contribution to As transformation were rarely estimated. Here, ROS formation and the redox conversion of As during the interaction between pyrite and arsenopyrite as function of O2, pH and pyrite surface oxidation were investigated. Pyrite promoted arsenopyrite oxidation and As(III) oxidation due to heterogeneous electron transfer. The electron transfer from arsenopyrite facilitated O2 reduction on pyrite surface with increasing ROS formation. Hydroxyl radical (HO˙), superoxide (O2•)- and hydrogen peroxide (H2O2) were the main reactive species for As(III) oxidation. Iron (hydr)oxides produced from pyrite surface oxidation provided fast electron transfer channels for efficient O2 reduction as evidenced by electrochemical experiment, further verifying the promoted effect of surface-oxidized pyrite (SOP) on arsenopyrite dissolution. However, total As and As(V) obviously decreased during SOP-mediated arsenopyrite oxidation. Iron (hydr)oxides retained appreciable As through adsorption to limit its mobility, and decreased HO˙ production to inhibit As(III) oxidation via decomposing H2O2. This work furthers our understanding of arsenic transformation in the environment which has important implications for mitigating arsenic pollution.

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.

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.

An improved U-net based retinal vessel image segmentation method.

Diabetic retinopathy is not just the most common complication of diabetes but also the leading cause of adult blindness. Currently, doctors determine the cause of diabetic retinopathy primarily by diagnosing fundus images. Large-scale manual screening is difficult to achieve for retinal health screen. In this paper, we proposed an improved U-net network for segmenting retinal vessels. Firstly, due to the lack of retinal data, pre-processing of the raw data is required. The data processed by grayscale transformation, normalization, CLAHE, gamma transformation. Data augmentation can prevent overfitting in the training process. Secondly, the basic network structure model U-net is built, and the Bi-FPN network is fused based on U-net. Datasets from a public challenge are used to evaluate the performance of the proposed method, which is able to detect vessel SP of 0.8604, SE of 0.9767, ACC of 0.9651, and AUC of 0.9787.

Reactive oxygen species formation driven by acidophiles mediated pyrite oxidation and its potential role on 2,4-dichlorophenol transformation.

Researches of reactive oxygen species (ROS) generation from pyrite oxidation and its impact on contaminants transformation has been constrained to abiotic conditions. However, pyrite oxidation by acidophiles is widespread in acidic environments. The potential role of these microorganisms on pyrite-induced ROS formation and pollutants processing is not understood well. Here, ROS production and 2,4-DCP transformation during pyrite oxidation under oxic and anoxic atmospheres by Acidithiobacillus ferrooxidans (A. ferrooxidans) were explored. 2,4-DCP removal was enhanced in biosystem. Under oxic and anoxic conditions, microbially mediated pyrite oxidation resulted in removing 93.66% and 43.77% 2,4-DCP, which were 1.14- and 1.51-fold greater than that without cells. Based on intermediates identified by LC-MS, the transformation pathway of 2,4-DCP was proposed. The trapping experiments demonstrated ROS contributed during 2,4-DCP transformation. The improving effect of A. ferrooxidans on 2,4-DCP degradation was mainly due to ROS increase. A. ferrooxidans was to promote pyrite surface renew, exposing more Fe(II) and Fe(III) sites that facilitated O2 reduction and H2O dissociation for ROS generation. Biogenic ROS and sulfite bio-oxidation with the free radical mechanism provided other ROS sources. ESR revealed A. ferrooxidans-pyrite interaction led to sustainable ROS production, indicating it could be a significant pathway in driving geochemical cycles of elements.

Insights into the role of extracellular DNA in heavy metal adsorption.

Extracellular polymeric substances (EPS) participate in heavy metal adsorption in the aquatic environments. Extracellular DNA (eDNA) is an essential component of EPS, but its involvement in metal binding remains ambiguous. Herein, the role of eDNA in Cd(II) and Ni(II) adsorption was described using a combination of semi-quantitative and qualitative approaches. EPS were extracted from Burkholderia sp. MBR-1 and eDNA accounted for 6.9% of the total mass of EPS. The eDNA in the extracted EPS was digested using the DNase II to prepare an eDNA-free EPS sample. Potentiometric titration unveiled that the number of total binding sites of the eDNA-free EPS was 19% lower than the untreated EPS. The Cd(II) and Ni(II) adsorption capacity of the eDNA-free EPS was lower than the untreated EPS at the pH range of 4-7. At pH 7, the results of batch adsorption experiments showed that removing eDNA from EPS resulted in declines of 12.6% and 15.7% in the adsorption capacities for Cd(II) and Ni(II), respectively. Furthermore, Fourier transform infrared spectroscopy (FTIR) and Raman spectroscopy unraveled that the phosphoryl groups and purines of eDNA are responsible for Cd(II) and Ni(II) complexation. The results demonstrated that eDNA plays an essential role in heavy metal adsorption.

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.

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.

Bioleaching and Electrochemical Behavior of Chalcopyrite by a Mixed Culture at Low Temperature.

Low-temperature biohydrometallurgy is implicated in metal recovery in alpine mining areas, but bioleaching using microbial consortia at temperatures <10°C was scarcely discussed. To this end, a mixed culture was used for chalcopyrite bioleaching at 6°C. The mixed culture resulted in a higher copper leaching rate than the pure culture of Acidithiobacillus ferrivorans strain YL15. High-throughput sequencing technology showed that Acidithiobacillus spp. and Sulfobacillus spp. were the mixed culture's major lineages. Cyclic voltammograms, potentiodynamic polarization and electrochemical impedance spectroscopy unveiled that the mixed culture enhanced the dissolution reactions, decreased the corrosion potential and increased the corrosion current, and lowered the charge transfer resistance and passivation layer impedance of the chalcopyrite electrode compared with the pure culture. This study revealed the mechanisms via which the mixed culture promoted the chalcopyrite bioleaching.

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.

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.

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.