Optical neural network can process information in parallel by using the technology based on free-space and integrated platform. Over the last half century, the development of integrated circuits has ...been limited by Moore's law. We know that neural network is based on the digital computer for successive calculation, most of which cannot be made into real-time processing system. Therefore, it is necessary to develop ONN for real-time processing and device miniaturization. In this paper, we review the progress of optical neural networks. Firstly, based on the principle of artificial neural networks, we elaborate the essence of optical matrix multiplier for linear operation. Then we introduce the optical neural network achieved by free-space optical interconnection and waveguide optical interconnection. Finally we talk about the nonlinearity in optical neural networks. With the gradual maturity of nanotechnology and the rapid advancement of silicon photonic integrated circuits, the progress of integrated photonic neural network has been promoted. Therefore, the construction of optical neural network on the future integrated photonic platform has potential application value.
In this paper, we present a deep learning method for single infrared image stripe noise removal. Our method is denoted as a deep convolutional neural network (CNN) that takes the noisy image as the ...input and outputs the clean image. The deep CNN consists of two components: 1) image denoising, substantially removing the stripe noise but losing details, 2) image denoising and super resolution, completely eliminating the residual stripe noise and restore details. Our deep CNN exhibits excellent image denoising and detail preserving performance. Meanwhile it achieves fast speed for real-time image processing. Experiments study the relationship between model parameter settings and model performance.
•A novel method for thermal infrared image colorization task is proposed.•A multi-term objection function can encourage an appealing image quality generation.•Extensive experiments demonstrate its ...superiority over existing methods.
Transforming a thermal infrared image into a realistic RGB image is a challenging task. In this paper we propose a deep learning method to bridge this gap. We propose learning the transformation mapping using a coarse-to-fine generator that preserves the details. Since the standard mean squared loss cannot penalize the distance between colorized and ground truth images well, we propose a composite loss function that combines content, adversarial, perceptual and total variation losses. The content loss is used to recover global image information while the latter three losses are used to synthesize local realistic textures. Quantitative and qualitative experiments demonstrate that our approach significantly outperforms existing approaches on the KAIST multispectral pedestrian dataset, achieving more plausible RGB images. Our code is available online22https://github.com/Kuangxd/TICCGAN..
In this paper, we present a simple and effective scene-based nonuniformity correction (NUC) method for infrared focal plane arrays based on interframe registration. This method estimates the global ...translation between two adjacent frames and minimizes the mean square error between the two properly registered images to make any two detectors with the same scene produce the same output value. In this way, the accumulation of the registration error can be avoided and the NUC can be achieved. The advantages of the proposed algorithm lie in its low computational complexity and storage requirements and ability to capture temporal drifts in the nonuniformity parameters. The performance of the proposed technique is thoroughly studied with infrared image sequences with simulated nonuniformity and infrared imagery with real nonuniformity. It shows a significantly fast and reliable fixed-pattern noise reduction and obtains an effective frame-by-frame adaptive estimation of each detector's gain and offset.
Infrared image enhancement is a crucial pre-processing technique in intelligent urban surveillance systems for Smart City applications. Existing grayscale mapping-based algorithms always suffer from ...over-enhancement of the background, noise amplification, and brightness distortion. To cope with these problems, an infrared image enhancement method based on adaptive histogram partition and brightness correction is proposed. First, the grayscale histogram is adaptively segmented into several sub-histograms by a locally weighted scatter plot smoothing algorithm and local minima examination. Then, the fore-and background sub-histograms are distinguished according to a proposed metric called grayscale density. The foreground sub-histograms are equalized using a local contrast weighted distribution for the purpose of enhancing the local details, while the background sub-histograms maintain the corresponding proportions of the whole dynamic range in order to avoid over-enhancement. Meanwhile, a visual correction factor considering the property of human vision is designed to reduce the effect of noise during the procedure of grayscale re-mapping. Lastly, particle swarm optimization is used to correct the mean brightness of the output by virtue of a reference image. Both qualitative and quantitative evaluations implemented on real infrared images demonstrate the superiority of our method when compared with other conventional methods.
The weathering of arsenopyrite in mine wastes is crucial for the formation of arsenic-containing acid mine drainage (AMD). Arsenopyrite is always associated with pyrite. In this study, the influence ...of pyrite on the biooxidation of arsenopyrite in the presence of Sulfobacillus thermosulfidooxidans was investigated by leaching experiments and surface characterizations. Bioleaching experiments of 5 days showed that the presence of pyrite increased the ferric concentration, and then accelerated arsenopyrite bioleaching, but increasing the mass ratio of pyrite to arsenopyrite above 1 had no obvious impact on further arsenopyrite dissolution. Synchrotron X-ray diffraction (S-XRD) and scanning electron microscopy (SEM) provided a direct understanding of arsenopyrite-pyrite galvanic interaction, and confirmed the formation of elemental sulfur and jarosite clusters, which did not passivate the arsenopyrite surface. Iron arsenate was detected by X-ray photoelectron spectroscopy (XPS) and X-ray absorption near edge structure (XANES). Additionally, the bioleaching experiments of 18 days indicated that the presence of pyrite facilitated the oxidation of As(III) to As(V) since most of arsenopyrite had been bioleached, and the increase of pyrite addition triggered the further oxidation of As(III).
In this study, we propose a method for object matching between visible and infrared images. We consider object matching between visible and infrared images as a computational patch-matching problem, ...and the main solution to this problem involves calculating the similarities of the relationships between the objects in the target and search images. Therefore, in this study, we propose a Siamese neural network, which takes a pair of visible and infrared images as the input. Our proposed Siamese network comprises a convolutional neural network (CNN) to ensure the effective extraction of features from visible and infrared images. The CNN comprises convolutional and pooling layers without padding. By calculating the cross-correlation of the objects in the visible image and those in the entire infrared image, we regard the parts with the highest similarity as the matched targets. During the training process, we use focal loss to solve the problem of the imbalance between the positive and negative samples in the dataset, after which we use interpolation to determine the locations of the target patches in the infrared images. We then conduct experiments on different classes of targets, and the results demonstrate that our proposed approach achieves greater accuracy and precision than other methods.
In this paper, the modified h-BN coating was coated on the prepreg sheets; then, the carbon fiber/epoxy composite laminates were prepared by compression molding. The through-thickness thermal ...conductivity, mechanical and thermomechanical properties of the laminates were studied, respectively. The results show that h-BN particles, which are flaky shape with a diameter of about 0.6 μm and thickness of about 70 nm, have been evenly incorporated into the composites, and the thermal stability, through-thickness thermal conductivity, impact strength and glass transition temperature (
T
g
) of the composites are improved accordingly. When the BN content is 10 m%, the initial thermal degradation temperature (
T
initial
) of the prepreg is 370 ℃, which is 10 ℃ higher than that of unmodified prepreg. When the BN content is 7 m%, the thermal conductivity of the laminates reaches 0.9 and 1.0 W·m
−1
·K
−1
at 25 and 100 ℃, respectively, which are 114% and 111% higher than that of pure laminate. When the content of BN is 5 m%, the impact strength of the laminate reaches the highest value of 225 kJ·m
−2
, which is 15.4% higher than that of pure laminate. BN particles form a dense heat conduction network, thus delaying the thermal degradation of the resin and improving the thermal conductivity. This study provides a facile path to fabricate composite laminates with the integrated structure and function.
Graphic abstract
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 2p
3/2
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.
In this study, the graphene-loaded LaFe
1-x
Cu
x
O
3
nanocomposites (GE/LaFe
1-x
Cu
x
O
3
) were prepared by the two-step method. The structure and properties of the samples were represented by X-ray ...diffractometer (XRD), Fourier-transform infrared spectroscopy (FTIR), Scanning electron microscope (SEM), Transmission electron microscope (TEM), X-ray photoelectron spectroscopy (XPS), and UV–Vis DRS, respectively. The results showed that the GE/LaFe
1-x
Cu
x
O
3
photocatalysts were prepared successfully, and the LaFe
0.7
Cu
0.3
O
3
particles were effectively decorated on the surface of GE. The UV–Vis DRS test showed the graphene and Cu
2+
co-doping modified LaFeO
3
photocatalyst had a high absorbance in the whole visible light range (380–780 nm) and the bandgap width of the co-doping materials reduced significantly. The photocatalytic test showed the methylene blue (MB) degradation rate of 3% GE/LaFe
0.7
Cu
0.3
O
3
under sunlight was 100% within 4 h, which was an increase of 75.4% than that of LaFeO
3
. The kinetic rate constant of 3% GE/LaFe
0.7
Cu
0.3
O
3
is 1.07 h
−1
, which is 5.69 times and 1.97 times higher than that of LaFeO
3
and LaFe
0.7
Cu
0.3
O
3
. The synergistic effect of Cu
2+
and GE on LaFeO
3
promotes the separation of photogenerated electrons and holes, thereby greatly improving the photocatalytic activity of LaFeO
3
. This work provided a new way to improve the efficiency of treating organic pollutants and broaden the photocatalytic application.
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