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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]

Wood structural timbers at three locations (eave, beam frame, and column) were exploited to elucidate the deterioration state and mechanism of historical timber structures from the Yunnan Military Academy (Kunming, Yunnan Province). The morphological structures, physical and mechanical characteristics, and chemical components were measured to illustrate the deterioration state and elucidate the degradation mechanism. The results demonstrated that the deterioration of structural timbers was closely dependent on their locations in the architecture. The outside eave was in the worst deterioration state, which was caused by natural factors (ultraviolet rays, rain, and fungi), resulting in severe distortion of wood structures and a significant reduction in basic density and compressive strength. Significant carbohydrate losses that occurred in the eave and beam frame were mainly contributed by the brown rot fungi, predominantly due to the frequently wet condition and humid environment. Moreover, column structures were mainly attacked by insects (weevils) due to their location near the ground, accordingly in an obvious decline in mechanical properties. Collectively, structural timbers exhibited different deterioration states contributed by the degradation mechanism, predominantly influenced by their locations in architecture. [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]

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]