Removing stripe noise is a fundamental task in remote sensing image processing, which is of great significance for improving image quality and subsequent applications. In this paper, an adaptive strip noise removal model is proposed with the spatial characteristics. Firstly, an adaptive weight function is constructed using local absolute differences to adaptively control the constraint intensity of the penalty term at different pixel points in the adaptive strip noise removal model. Secondly, L1 norm is used to constrain the local smoothness along the direction of the strip, maintaining the obvious smoothness characteristics of the strip noise in its extension direction, while L2 norm is used to restrict the image grayscale. Finally, the extended split Bregman iteration method and alternating minimization method is used to optimize the proposed image destriping model. Extensive experiments on both the synthetic and real remote sensing images validate that the proposed model can effectively remove the stripe noise and preserve more fine scale details.
Multi-view stereo is a method that analyzes and processes images from multiple perspectives to estimate the 3D geometric information of the scene to achieve 3D reconstruction. To improve the accuracy of 3D reconstruction in large-scale scenes and reduce the complexity of the reconstruction algorithm, in this paper, we propose a coarse-to-fine multi-view stereo network based on attention mechanism. First, we use a feature pyramid to extract multi-scale features, introducing richer geometric information and more contextual information at different levels of the pyramid to improve modeling accuracy. Then, we use position encoding on the coarse-scale feature map and introduce an attention mechanism to obtain more context information. We adopt a cascade structure to achieve high-resolution depth map construction. We use the reference image to refine the final result again and enhance details such as edges. We conduct experiments on the publicly available DTU dataset. Experimental results show that our proposed method improves accuracy compared with existing algorithms. In addition, we also conduct experiments on other representative public datasets. The accuracy of the experimental results further validates the effectiveness of our proposed method.
3D object detection from lidar point cloud is a key technology in autonomous driving. As the sparseness, irregular and large amount of lidar point cloud data, which lead to slow operation efficiency of neural network and low detection accuracy. In order to address this problem, we research on vehicle object detection form lidar point cloud in this paper. In the data preprocessing stage, we use the point cloud reduction method based on density clustering (DBSCAN) to remove the sparse outliers points and noise points from the point cloud, and better retain the target features. The simplified point cloud makes the network converge faster in the training phase, effectively reduces the network computing overhead, and reduces the training time by ~40%. In order to make the network to get a better detection ability, we also add an attention network (Point Attention) to learn the key features from the target point cloud. The experimental results show that our proposed method successfully improves the network operation efficiency, and the accuracy of vehicle object detection is also increased to 89.5%.
In modern control system, PID, proportional-integral-derivative control, is normally used according to its straightforward structure, strong adaptability, and easy manipulation. However, in the real world, traditional PID controller might meet issues like low stability, weak anti-interference ability, and unsatisfying response capability. With the aim of solving the mentioned problems, fuzzy method is be applied into PID controlling and anti-interference technology is added as well. Firstly, according to the original physical model, mathematical model is established, and the transfer function is calculated out by applying appropriate motor parameters. Secondly, the methodology of fuzzy control is added into the servo motor system so self-adaptive PID controller is achieved. Finally, a concise comparison of the simulation results is carried out and our proposed fuzzy PID is proved to perform much better than the traditional one.
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