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• Polystyrene nanoplastics (PSNPs) significantly affected the growth and feed intake. • DEHP-PSNPs co-exposure significantly increased PSNPs retention in the intestines. • DEHP-PSNPs co-exposure caused significant oxidative stress and histological damages. • DEHP-PSNPs co-exposure changed the intestinal microbiota composition. Organic pollutants such as di-(2-ethylhexyl) phthalate (DEHP) interact with nanoplastics (NPs) and change their bioavailability and toxicity to aquatic organisms. This study aims to assess the ecotoxicological impacts of NPs in the presence and absence of DEHP on juvenile largemouth bass (LMB) Micropterus salmoides. Therefore, LMB was fed with diets containing various concentrations (0, 2, 10, and 40 mg/g) of polystyrene nanoplastics (PSNPs) by the weight of diets. After a 21-day of PSNPs dietary exposure, LMB was treated with DEHP at 450 μg/L through waterborne exposure for three days. Our results showed that PSNPs were accumulated in the intestinal tissues, which significantly decreased the feeding and growth rates in LMB. The histopathological analysis showed the intestine and liver of LMB were subjected to various degrees of structural damage caused by PSNPs, and DEHP-PSNP co-exposure enhanced those histopathological damages in both tissues. Additionally, the co-exposure induced oxidative stress in terms of increased activities of glutathione S-transferase, catalase, and superoxide dismutase enzymes in the liver, intestine, spleen, and serum. Furthermore, the co-exposure significantly changed the intestinal microbial composition, i.e., the decrease in the abundance of probiotics (Bacteroidetes and Proteobacteria) and the increase in pathogenic bacteria (Firmicutes) posed a great threat to fish metabolism and health. Therefore, this study highlights that the presence of DEHP enhances the toxicity of NPs on LMB in freshwater and suggests the regulated use of plastic and its additives for improving the health status of aquaculture fish for food safety in humans. [Display omitted] [ABSTRACT FROM AUTHOR]

With the rapid development of infrared technology, infrared cameras are widely used in the military, medical, and industrial fields. However, the thermal radiation information received by infrared cameras often undergoes degradation due to random factors such as scattering or reflection during transmission. Consequently, the final infrared image suffers from poor contrast and blurred vision. To address these problems, this paper proposes an infrared image contrast enhancement algorithm based on the atmospheric scattering model. Firstly, we modify the hypothesis of the dark channel prior theory, and the pseudo dark channel prior theory is deduced to meet the processing requirements of single-channel images. Secondly, we propose anti-target interference and filtering threshold method to accurately estimate the atmospheric illumination value and transmittance of the atmospheric scattering model. These methods effectively mitigate the influence of infrared targets on the atmospheric illumination value and eliminate the blocking artifact effect caused by pixel value jumps in the transmittance images. Furthermore, we employ adaptive histogram equalization with contrast limitation, highlighting the global contrast. This paper conducts experiments on the M3FD dataset and employs objective evaluations using Visible Edge (E), Figure Definition (FD), Peak Signal-to-Noise Ratio (PSNR), Information Entropy (IE), Structure Similarity Index Measure (SSIM), and Visual Information Fidelity (VIF). The results indicate that the proposed algorithm enhances the overall contrast of images while highlighting fine texture details, exhibiting good visual effects. Both subjective and objective evaluations outperform seven other contrast algorithms, demonstrating the effectiveness of the proposed method. [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]