Sheng Jiang

Bio: Sheng Jiang is an academic researcher. The author has contributed to research in topics: Computer science & Pattern recognition (psychology). The author has an hindex of 2, co-authored 6 publications receiving 10 citations.

Infrared and visible image fusion based on QNSCT and Guided Filter

Abstract: Image fusion is the process of fusing multiple images of the same scene to obtain a more informative image for human eye perception. In this paper, a new fusion framework based on Quaternion Non-Subsampled Contourlet Transform (QNSCT) and Guided Filter detail enhancement is designed to address the problems of inconspicuous infrared targets and poor background texture in Infrared and visible image fusion. The proposed method uses the quaternion wavelet transform for the first time instead of the traditional Non-Subsampled Pyramid Filter Bank structure in the Non-Subsampled Contourlet Transform (NSCT). The flexible multi-resolution of quaternion wavelet and the multi-directionality of NSCT are fully utilized to refine the multi-scale decomposition scheme. On the other hand, the coefficient matrix obtained from the proposed QNSCT algorithm is fused using a weight refinement algorithm based on the guided filter. The fusion scheme is divided into four steps. First, the Infrared and visible images are decomposed into multi-directional and multiscale coefficient matrices using QNSCT. The experimental results show that the proposed algorithm not only extracts important visual information from the source image, but also preserves the texture information in the scene better. Meanwhile, the scheme outperforms state-of-the-art methods in both subjective and objective evaluations.

Dual-Band Terahertz Perfect Absorber Based on Metal Micro-Nano Structure

Abstract: We propose a sub-wavelength range-based dual-band tunable ideal terahertz metamaterial perfect absorber. The absorber structure consists of three main layers, with the absorber layer consisting of a metal I-shaped structure. By simulating the incident wave absorbance of the structure, we found that the structure has more than 99% absorption peaks in both bands. In addition, we have investigated the relationship between structural absorbance and the structural geometrical parameters. We have studied the relationship between the thickness of the metal absorber layer hb and the absorbance of the metamaterial structure in the 4–14 THz band. Secondly, we have studied the relationship between the thickness of the SiO2 dielectric layer and structural absorbance. Afterwards, we have studied the relationship between the incident angle of the incident electromagnetic wave and structural absorbance. Finally, we have studied the relationship between the length of the metal structure and structural absorbance. The structure can be effectively used for detectors, thermal emitters, terahertz imaging and detection.

Single Infrared Image Stripe Removal via Residual Attention Network

Abstract: The non-uniformity of the readout circuit response in the infrared focal plane array unit detector can result in fixed pattern noise with stripe, which seriously affects the quality of the infrared images. Considering the problems of existing non-uniformity correction, such as the loss of image detail and edge blurring, a multi-scale residual network with attention mechanism is proposed for single infrared image stripe noise removal. A multi-scale feature representation module is designed to decompose the original image into varying scales to obtain more image information. The product of the direction structure similarity parameter and the Gaussian weighted Mahalanobis distance is used as the similarity metric; a channel spatial attention mechanism based on similarity (CSAS) ensures the extraction of a more discriminative channel and spatial feature. The method is employed to eliminate the stripe noise in the vertical and horizontal directions, respectively, while preserving the edge texture information of the image. The experimental results show that the proposed method outperforms four state-of-the-art methods by a large margin in terms of the qualitative and quantitative assessments. One hundred infrared images with different simulated noise intensities are applied to verify the performance of our method, and the result shows that the average peak signal-to-noise ratio and average structural similarity of the corrected image exceed 40.08 dB and 0.98, respectively.

Bloodstain Identification Based on Visible/Near-Infrared Hyperspectral Imaging With Convolutional Neural Network

Abstract: Blood samples are easily damaged in traditional bloodstain detection and identification. In complex scenes with interfering objects, bloodstain identification may be inaccurate, with low detection rates and false-positive results. In order to meet these challenges, we propose a bloodstain detection and identification method based on hyperspectral imaging and mixed convolutional neural networks, which enables fast and efficient non-destructive identification of bloodstains. In this study, we apply visible/near-infrared reflectance hyperspectral imaging in the 380–1000 nm spectral region to analyze the shape, structure, and biochemical characteristics of bloodstains. Hyperspectral images of bloodstains on different substrates and six bloodstain analogs are experimentally obtained. The acquired spectral pixels are pre-processed by Principal Component Analysis (PCA). For bloodstains and different bloodstain analogs, regions of interest are selected from each substance to obtain pixels, which are further used in convolutional neural network (CNN) modeling. After the mixed CNN modeling is completed, pixels are selected from the hyperspectral images as a test set for bloodstains and bloodstain analogs. Finally, the bloodstain recognition ability of the mixed 2D-3D CNN model is evaluated by analyzing the kappa coefficient and classification accuracy. The experimental results show that the accuracy of the constructed CNN bloodstain identification model reaches 95.4%. Compared with other methods, the bloodstain identification method proposed in this study has higher efficiency and accuracy in complex scenes. The results of this study will provide a reference for the future development of the bloodstain online detection system.

Analysis of X-Ray Beam Hardening Correction Method Relying on Cloud Computing Big Data

Abstract: Regarding the correction of X-ray beam hardening in the current CT imaging system, the traditional method will cause the overlapping of images during use, which will gradually harden the beam, and the image reconstructed by the imaging system will gradually become “cup-shaped” or “striped.” “False images” seriously degrade the quality of the images, while causing more interfering diagnostic problems. In this paper, the cloud computing big data analysis algorithm is applied to the X-ray beam hard correction process. According to the transition energy and its energy absorption, the X-ray beam is used as the cut-in point, and the relationship between the attenuation coefficient of similar materials and the X-ray energy is used to remove the artifact image in the initial image reconstruction process to obtain a clear corrected image. Meanwhile, according to the thickness and gray value of the X-ray penetrable object, the result of fitting using a polynomial function is calculated, and an accurate line fitting can be completed for data with smaller coordinates. Finally, the experimental study shows that the cloud computing big data analysis proposed in this paper can detect X-ray beams in real time. This method uses optical receivers to achieve high noise sensitivity to X-rays, and in long-distance transmission scenarios, the bandwidth of communication transmission can be maximized, and different types of formats can be used to complete modulation for different X-rays. Therefore, X-ray beam hardening correction technology has better advantages and the market application scenarios compared with other technologies.

Switchable terahertz absorber from single broadband to triple-narrowband

Abstract: We proposed a switchable multifunctional terahertz (THz) absorber based on graphene and vanadium dioxide (VO 2 ), and investigated the absorption properties and the tuning of the absorber by using Computer Simulation Technology (CST) microwave studio. It was found that, when VO 2 is in dielectric phase, the structure shows a single broadband absorption from 0.8 to 2.4 THz range. The amplitude of the absorption can be tuned from 95 % to 20 % by changing the Fermi energy level of the graphene. And the absorption is found to be insensitive to the polarization angle and the incident angle. When VO 2 is in metallic phase, the absorber shows triple narrowband absorption. Utilizing impedance matching theory and transmission line theory, the physical mechanism of the absorber is investigated. • The designed structure is simple and easy to make in practice. • The proposed absorber has high efficient absorptivity. • The absorber enables functional switching between broadband and triple narrowband. • The absorber can achieve dynamic tuning of the single broadband absorptance.

Application of Machine Learning in Multi-Directional Model to Follow Solar Energy Using Photo Sensor Matrix

Abstract: In this paper, we introduce a deep neural network (DNN) for forecasting the intra-day solar irradiance, photovoltaic PV plants, regardless of whether or not they have energy storage, can benefit from the work being done here. The proposed DNN utilises a number of different methodologies, two of which are cloud motion analysis and machine learning, in order to make forecasts regarding the climatological conditions of the future. In addition to this, the accuracy of the model was evaluated in light of the data sources that were easily accessible. In general, four different cases have been investigated. According to the findings, the DNN is capable of making more accurate and reliable predictions of the incoming solar irradiance than the persistent algorithm. This is the case across the board. Even without any actual data, the proposed model is considered to be state-of-the-art because it outperforms the current NWP forecasts for the same time horizon as those forecasts. When making predictions for the short term, using actual data to reduce the margin of error can be helpful. When making predictions for the long term, however, weather information can be beneficial.

Dielectric Cavity-Insulator-Metal (DCIM) Metamaterial Absorber in Visible Range

Abstract: For many years, metamaterial absorbers have received much attention in a wide range of application fields. There is an increasing need to search for new design approaches that fulfill more and more complex tasks. According to the specific application requirements, design strategy can vary from structure configurations to material selections. A new combination of a dielectric cavity array, dielectric spacer, and gold reflector as a metamaterial absorber is proposed and theoretically studied in this work. The complexity of the dielectric cavities leads to a more flexible optical response than traditional metamaterial absorbers. It gives a new dimension of freedom for a real three-dimensional metamaterial absorber design.