DCANet: Dual Convolutional Neural Network with Attention for Image Blind Denoising

Noise removal of images is an essential preprocessing procedure for many computer vision tasks. Currently, many denoising models based on deep neural networks can perform well in removing the noise with known distributions (i.e. the additive Gaussian white noise). However eliminating real noise is still a very challenging task, since real-world noise often does not simply follow one single type of distribution, and the noise may spatially vary. In this paper, we present a new dual convolutional neural network (CNN) with attention for image blind denoising, named as the DCANet. To the best of our knowledge, the proposed DCANet is the first work that integrates both the dual CNN and attention mechanism for image denoising. The DCANet is composed of a noise estimation network, a spatial and channel attention module (SCAM), and a CNN with a dual structure. The noise estimation network is utilized to estimate the spatial distribution and the noise level in an image. The noisy image and its estimated noise are combined as the input of the SCAM, and a dual CNN contains two different branches is designed to learn the complementary features to obtain the denoised image. The experimental results have verified that the proposed DCANet can suppress both synthetic and real noise effectively. The code of DCANet is available at https://github.com/WenCongWu/DCANet

Two-stage Progressive Residual Dense Attention Network for Image Denoising

Deep convolutional neural networks (CNNs) for image denoising can effectively exploit rich hierarchical features and have achieved great success. However, many deep CNN-based denoising models equally utilize the hierarchical features of noisy images without paying attention to the more important and useful features, leading to relatively low performance. To address the issue, we design a new Two-stage Progressive Residual Dense Attention Network (TSP-RDANet) for image denoising, which divides the whole process of denoising into two sub-tasks to remove noise progressively. Two different attention mechanism-based denoising networks are designed for the two sequential sub-tasks: the residual dense attention module (RDAM) is designed for the first stage, and the hybrid dilated residual dense attention module (HDRDAM) is proposed for the second stage. The proposed attention modules are able to learn appropriate local features through dense connection between different convolutional layers, and the irrelevant features can also be suppressed. The two sub-networks are then connected by a long skip connection to retain the shallow feature to enhance the denoising performance. The experiments on seven benchmark datasets have verified that compared with many state-of-the-art methods, the proposed TSP-RDANet can obtain favorable results both on synthetic and real noisy image denoising. The code of our TSP-RDANet is available at https://github.com/WenCongWu/TSP-RDANet

Distributed Fixed-Time Triggering-Based Containment Control for Networked Nonlinear Agents under Directed Graphs

This paper discusses the distributed fixed-time containment control for networked nonlinear systems via the event/self-triggered approaches over directed graphs. A distributed event-triggered control protocol without continuous control updates is first proposed. In order to relieve the chattering effect, a modified distributed event-triggered control law is developed. To further overcome the drawback of continuous state monitoring and reduce the communication frequency between neighboring agents, a distributed chattering-free self-triggered control strategy is accordingly designed. A favorable aspect of our work is that the unnecessary resource utilizations of computation, communication as well as control updates can be saved while sustaining the desired control properties and excluding the Zeno behavior. Another distinct feature of this paper lies in that the containment control objective is realized in fixed time and the estimate of settling time can be prescribed without dependence on initial states of networked agents. Finally, some simulation results are provided to illustrate the effectiveness of the theoretical control schemes

Automatic Positioning of Street Objects Based on Self-Adaptive Constrained Line of Bearing from Street-View Images

In order to realize the management of various street objects in smart cities and smart transportation, it is very important to determine their geolocation. Current positioning methods of street-view images based on mobile mapping systems (MMSs) mainly rely on depth data or image feature matching. However, auxiliary data increase the cost of data acquisition, and image features are difficult to apply to MMS data with low overlap. A positioning method based on threshold-constrained line of bearing (LOB) overcomes the above problems, but threshold selection depends on specific data and scenes and is not universal. In this paper, we propose the idea of divide–conquer based on the positioning method of LOB. The area to be calculated is adaptively divided by the driving trajectory of the MMS, which constrains the effective range of LOB and reduces the unnecessary calculation cost. This method achieves reasonable screening of the positioning results within range without introducing other auxiliary data, which improves the computing efficiency and the geographic positioning accuracy. Yincun town, Changzhou City, China, was used as the experimental area, and pole-like objects were used as research objects to test the proposed method. The results show that the 6104 pole-like objects obtained through object detection realized by deep learning are mapped as LOBs, and high-precision geographic positioning of pole-like objects is realized through region division and self-adaptive constraints (recall rate, 93%; accuracy rate, 96%). Compared with the existing positioning methods based on LOB, the positioning accuracy of the proposed method is higher, and the threshold value is self-adaptive to various road scenes

A Spherical Volume-Rendering Method of Ocean Scalar Data Based on Adaptive Ray Casting

There are some limitations in traditional ocean scalar field visualization methods, such as inaccurate expression and low efficiency in the three-dimensional digital Earth environment. This paper presents a spherical volume-rendering method based on adaptive ray casting to express ocean scalar field. Specifically, the minimum bounding volume based on spherical mosaic is constructed as the proxy geometry, and the depth texture of the seabed terrain is applied to determine the position of sampling points in the spatial interpolation process, which realizes the fusion of ocean scalar field and seabed terrain. Then, we propose an adaptive sampling step algorithm according to the heterogeneous depth distribution and data change rate of the ocean scalar field dataset to improve the efficiency of the ray-casting algorithm. In addition, this paper proposes a nonlinear color-mapping enhancement scheme based on the skewness characteristics of the datasets to optimize the expression effect of volume rendering, and the transparency transfer function is designed to realize volume rendering and local feature structure extraction of ocean scalar field data in the study area

Estimating the Photovoltaic Potential of Building Facades and Roofs Using the Industry Foundation Classes

Photovoltaic energy generation has gained wide attention owing to its efficiency and environmental benefits. Therefore, it has become important to accurately evaluate the photovoltaic energy generation potential of building surfaces. As the number of building floors increases, the area of the facades becomes much larger than that of the roof, providing improved potential for photovoltaic equipment installation. Conventional urban solar potential evaluation methods are usually based on light detection and ranging (LiDAR). However, LiDAR can only be used in existing buildings, and the lack of semantic information in the point cloud data generated by LiDAR makes it impossible to evaluate the photovoltaic potential of facades (including details such as windows) in detail and with accuracy. In this study, we developed a method to accurately extract facades and roofs in order to evaluate photovoltaic potential based on the Industry Foundation Classes. To verify the feasibility of this approach, we used a building from Xuzhou city, Jiangsu province, China. The simulation results indicate that, out of the total building photovoltaic installable area (8995 m2), that of the facade is 8240 m2. The photovoltaic potential of the simulated building could reach 1054.69 MWh/year. The sensitivity studies of the grid resolution, the time interval and the computation time confirmed the reasonability of the determined conditions. The method proposed offers great potential for energy planning departments and the improved utilization of buildings

Numeral simulation of solution mixing effect and product preparation effect in the process of preparing cerium oxide by microwave heating

Microwave heating has the characteristics of simple control and high efficiency, in which electromagnetic force plays an important role. Using the UDF function and energy equation in FLUENT software, the electromagnetic force model in the tube is established. The mixing process of cerium chloride solution under different heating modes, the mixing effect of Lorentz force and the preparation process of cerium oxide under different inlet material velocities were numerically simulated. CFD-Post and MATLAB software are used to post-process the data of the whole fluid area. Through the above analysis, the best process conditions of numerical simulation and experiment are finally determined. The results show that microwave heating has better mixing effect and lower material loss compared with conventional heating, in which electromagnetic force plays a promoting role; Under the condition of microwave heating, the product conversion rate is high, and the concentration of cerium oxide is high and distributed evenly. The more appropriate mixing method of cerium chloride and preparation method of cerium oxide are microwave heating, and the more appropriate inlet velocity of materials is 0.15 m/s

Multi-Scale Flow Field Mapping Method Based on Real-Time Feature Streamlines

Traditional static flow field visualization methods suffer from many problems, such as a lack of continuity expression in the vector field, uneven distribution of seed points, messy visualization, and time-consuming calculations. In response to these problems, this paper proposes a multi-scale mapping method based on real-time feature streamlines. The method uses feature streamlines to solve the problem of continuity expression in flow fields and introduces a streamline tracking algorithm which combines adaptive step length with velocity matching to render feature streamlines in a real-time and multi-scale way. In order to improve the stability and uniformity of the seed point layout, this method uses two different point placement methods which utilize a global regular grid distribution algorithm and feature area random distribution algorithm. In addition, this method uses a collision detection algorithm to detect and deal with the unreasonable covering between streamlines, which alleviates visual confusion in the resulting drawing. This method also uses HTML5 Canvas to render streamlines, which greatly improves the drawing speed. Therefore, use of this method can not only improve the uniformity of the seed point layout and the speed of drawing but also solve the problems of continuity expression in the vector field and messy visualization

The plasma mitochondrial DNA is an independent predictor for post-traumatic systemic inflammatory response syndrome.

BACKGROUND AND PURPOSE: Mitochondrial DNA (mtDNA), a newly identified damage-associated molecular pattern, has been observed in trauma patients, however, little is known concerning the relationship between plasma mtDNA levels and concrete post-traumatic complications, particularly systemic inflammatory response syndrome (SIRS). The aim of this study is to determine whether plasma mtDNA levels are associated with injury severity and cloud predict post-traumatic SIRS in patients with acute traumatic injury. PATIENTS AND METHODS: Eighty-six consecutive patients with acute traumatic injury were prospectively enrolled in this study. The plasma mtDNA concentration was measured by a real-time, quantitative PCR assay for the human ND2 gene. The study population's clinical and laboratory data were analyzed. RESULTS: The median plasma mtDNA was higher in trauma patients than in healthy controls (865.196 (251.042-2565.40)pg/ml vs 64.2147 (43.9049-80.6371)pg/ml, P<0.001) and was independently correlated with the ISS score (r=0.287, P<0.001). The plasma mtDNA concentration was also significantly higher in patients who developed post-traumatic SIRS than in patients who did not (1774.03 (564.870-10901.3)pg/ml vs 500.496 (145.415-1285.60)pg/ml, P<0.001). Multiple logistic regression analysis revealed that the plasma mtDNA was an independent predictors for post-traumatic SIRS (OR, 1.183 (95%CI, 1.015-1.379), P=0.032). Further ROC analysis demonstrated that a high plasma mtDNA level predicted post-traumatic SIRS with a sensitivity of 67% and a specificity of 76%, with a cut-off value of 1.3185 µg/ml being established, and the area under the ROC curves (AUC) was 0.725 (95% CI 0.613-0.837). CONCLUSIONS: Plasma mtDNA was an independent indictor with moderate discriminative power to predict the risk of post-traumatic SIRS