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This work aims to explore the characteristics of stochastic fluctuant water pressure acted on the surface of large radial gate, and to investigate the flow-induced vibration response of the whole radial gate structure. The finite element calculation model structure of the radial gate is established by taking a large-scale radial gate as prototype to discuss the hydrodynamic pressure acting on the gate leaf with different opening, analyze the dynamic pressure time curves, and achieve the flow-induced vibration response by deeming hydrodynamic pressure as dynamic load. When taking 10% opening of the radial gate, the results indicate that the hydrodynamic pressure distributed on the arc surface of the radial gate changes with the flow conditions, with the maximum pressure occurred at the center of the lower edge of the gate leaf. One point in the time history curve of fluctuating water pressure can be taken as the dynamic load for the flow-induced vibration analysis. The flow-induced vibration responses at the monitoring point of the radial gate structure show periodic changes in the X, Y, and Z directions. The finite element simulation results agree well with the theoretical calculation results, so reference can be provided for the hydrodynamic pressure testing and the flow-induced vibration response calculations. [ABSTRACT FROM AUTHOR]

Because of the unique imaging mechanism of infrared (IR) sensors, IR images commonly suffer from blurred edge details, low contrast, and poor signal-to-noise ratio. A new method is proposed in this paper to enhance IR image details so that the enhanced images can effectively inhibit image noise and improve image contrast while enhancing image details. First, for the traditional guided image filter (GIF) applied to IR image enhancement is prone to halo artifacts, this paper proposes a detail enhancement guided filter (DGIF). It mainly adds the constructed edge perception and detail regulation factors to the cost function of the GIF. Then, according to the visual characteristics of human eyes, this paper applies the detail regulation factor to the detail layer enhancement, which solves the problem of amplifying image noise using fixed gain coefficient enhancement. Finally, the enhanced detail layer is directly fused with the base layer so that the enhanced image has rich detail information. We first compare the DGIF with four guided image filters and then compare the algorithm of this paper with three traditional IR image enhancement algorithms and two IR image enhancement algorithms based on the GIF on 20 IR images. The experimental results show that the DGIF has better edge-preserving and smoothing characteristics than the four guided image filters. The mean values of quantitative evaluation of information entropy, average gradient, edge intensity, figure definition, and root-mean-square contrast of the enhanced images, respectively, achieved about 0.23%, 3.4%, 4.3%, 2.1%, and 0.17% improvement over the optimal parameter. It shows that the algorithm in this paper can effectively suppress the image noise in the detail layer while enhancing the detail information, improving the image contrast, and having a better visual effect. [ABSTRACT FROM AUTHOR]

[Display omitted] • Firstly, the local linear model established in this paper presents the gradient information of the VIS image as the pixel intensity of the IR image. • Secondly, the detail perception factor and contrast control factor are introduced into the design of the cost function, which has the effect of enhancing contrast and suppressing noise moderately • Finally, this paper adopts the strategy of dual-scale decomposition to obtain infrared features and combine them with the initial fusion results. This scheme enriches the detailed texture information from the IR image in the fusion result. Infrared (IR) and visible (VIS) images represent the features of the scene at different wavelengths, and the features they contain have different properties. Therefore, the traditional weighted fusion strategy is challenging to preserve the different types of feature information. In addition, VIS images are highly susceptible to bad weather, which also seriously affects the quality of fused images in complex environments. To solve the above problems, we propose a feature enhancement fusion method. First, a fusion model called contrast enhancement guided filter (CEGF) is proposed. The new model enables the texture information of VIS images to be presented with the intensity of infrared pixels, which solves the problem of combining different attribute features and removes the influence of harsh lighting conditions. To improve the visibility of texture details under different lighting conditions, a contrast modulation factor is added to the cost function design of the filter to enhance the contrast of visible details. Second, we use a dual-scale decomposition strategy to enhance the infrared feature information of the fusion results. Finally, we apply the method of this paper with ten classical image fusion algorithms in two types of datasets. The visual effect and objective evaluation of the fusion results verify that the proposed method preserves the characteristics of the high contrast of IR images and improves the visibility of infrared scenes for subsequent target identification and detection. [ABSTRACT FROM AUTHOR]

The Degree of Linear Polarization (DoLP) is a key dimension of information in polarization imaging. The quality of polarization images is greatly affected by imaging equipment and environmental factors. Therefore, an enhancement method for the DoLP image is proposed in this paper. First, a super-resolution reconstruction algorithm based on the Detail Enhancement Guided Image Filter is proposed to solve the problem of mosaicization and pseudo-polarization information caused by the degradation of spatial resolution and super-resolution reconstruction of DoFP imaging. Second, for the problem of rapid degradation of polarization image quality caused by hazy environment, a DoLP enhancement algorithm based on multi-channel difference Signal-to-Noise Ratio is proposed to improve the visual effect of polarization images in different scenes with hazy environment. The method proposed in this paper implements super-resolution reconstruction, demosaicing, pseudo-polarization information removal, and adaptive enhancement of DoLP images in DoFP systems. [ABSTRACT FROM AUTHOR]

The outbreak of jellyfish blooms poses a serious threat to human life and marine ecology. Therefore, jellyfish detection techniques have earned great interest. This paper investigates the jellyfish detection and classification algorithm based on optical images and deep learning theory. Firstly, we create a dataset comprising 11,926 images. A MSRCR underwater image enhancement algorithm with fusion is proposed. Finally, an improved YOLOv4-tiny algorithm is proposed by incorporating a CBMA module and optimizing the training method. The results demonstrate that the detection accuracy of the improved algorithm can reach 95.01%, the detection speed is 223FPS, both of which are better than the compared algorithms such as YOLOV4. In summary, our method can accurately and quickly detect jellyfish. The research in this paper lays the foundation for the development of an underwater jellyfish real-time monitoring system. [ABSTRACT FROM AUTHOR]

The outbreak of jellyfish blooms poses a serious threat to human life and marine ecology. Therefore, jellyfish detection techniques have earned great interest. This paper investigates the jellyfish detection and classification algorithm based on optical images and deep learning theory. Firstly, we create a dataset comprising 11,926 images. A MSRCR underwater image enhancement algorithm with fusion is proposed. Finally, an improved YOLOv4-tiny algorithm is proposed by incorporating a CBMA module and optimizing the training method. The results demonstrate that the detection accuracy of the improved algorithm can reach 95.01%, the detection speed is 223FPS, both of which are better than the compared algorithms such as YOLOV4. In summary, our method can accurately and quickly detect jellyfish. The research in this paper lays the foundation for the development of an underwater jellyfish real-time monitoring system. [ABSTRACT FROM AUTHOR]

Due to the low spatial resolution of the existing optical micro-scanning thermal microscope imaging system, the acquired micro-scanning infrared images have inferior image quality and low contrast. Deep learning methods, represented by SRGAN, have shown promising results in super-resolution. However, this method still has artifacts, blurriness, low spatial resolution, and slow reconstruction speed. Therefore, we propose the SMC-SRGAN-Lightning super-resolution algorithm based on optical micro-scanning thermal microscope images in this study. Firstly, we enhance the model’s attention to features and improve the details and clarity of the reconstructed images. Removing the BN layer in residual blocks, replacing the ReLU with SMU, and introducing the CBAM to construct the SMC module. Secondly, we incorporate the attention mechanism SEnet into the Bottleneck structure of MobileNetV2. Reducing the channels in the first 1 × 1 convolution layer to 1/4 and creating the SE-MobileNetV2 module. It can enhance the model’s focus on essential features, computational efficiency, and accuracy. Finally, to validate the effectiveness of our method, we compare it with four other super-resolution algorithms on public datasets and images obtained from the optical micro-scanning thermal microscope imaging system. Experimental results indicate that our method improves image clarity, preserving details, and textures. Comprehensively considering super-resolved image quality and time costs, our method is superior to other methods. [ABSTRACT FROM AUTHOR]

In recent years, jellyfish outbreaks have frequently occurred in offshore areas worldwide, posing a significant threat to the marine fishery, tourism, coastal industry, and personal safety. Effective monitoring of jellyfish is a vital method to solve the above problems. However, the optical detection method for jellyfish is still in the primary stage. Therefore, this paper studies a jellyfish detection method based on convolution neural network theory and digital image processing technology. This paper studies the underwater image preprocessing algorithm because the quality of underwater images directly affects the detection results. The results show that the image quality is better after applying the three algorithms namely prior defogging, adaptive histogram equalization, and multi-scale retinal enhancement, which is more conducive to detection. We establish a data set containing seven species of jellyfishes and fish. A total of 2141 images are included in the data set. The YOLOv3 algorithm is used to detect jellyfish, and its feature extraction network Darknet53 is optimized to ensure it is conducted in real-time. In addition, we introduce label smoothing and cosine annealing learning rate methods during the training process. The experimental results show that the improved algorithms improve the detection accuracy of jellyfish on the premise of ensuring the detection speed. This paper lays a foundation for the construction of an underwater jellyfish optical imaging real-time monitoring system. [ABSTRACT FROM AUTHOR]