Advances in Multimedia

Image dehazing is one of the problems that need to be solved urgently in the field of computer vision. In recent years, more and more algorithms have been applied to image dehazing and achieved good results. However, the image after dehazing still has color distortion, contrast and saturation disorder, and other challenges; in order to solve these problems, in this paper, an effective image dehazing method is proposed, which is based on improved color channel transfer and multiexposure image fusion to achieve image dehazing. First, the image is preprocessed using a color channel transfer method based on k-means. Second, gamma correction is introduced on the basis of guided filtering to obtain a series of multiexposure images, and the obtained multiexposure images are fused into a dehazed image through a Laplacian pyramid fusion scheme based on local similarity of adaptive weights. Finally, contrast and saturation corrections are performed on the dehazed image. Experimental verification is carried out on synthetic dehazed images and natural dehazed images, and it is verified that the method proposed is superior to existing dehazed algorithms from both subjective and objective aspects.

With the rapid development of the national power grid, there is an increasing and strict demand for accurate intelligent management. However, the current detection algorithms have limited abilities under adverse conditions, especially in regions like Yunnan Province with complex terrain. To address this issue, we propose a method that utilizes infrared and visible images to make the images more informative, thereby improving the accuracy of the detection algorithm for electric power construction site safety. First, we design channel attention (CA) module and pixel attention (PA) module to focus on more important channels and resist thick haze pixels that focus on the thick haze pixels and more important channel information. Furthermore, we design a two-stage discriminator which imposes two restrictions on the fused results. Finally, we conduct a large number of comparison experiments with state-of-the-art methods, and the results show that our proposed fusion method achieves excellent performance in infrared and visible image fusion. This method has good prospects for application in the safety supervision of power construction sites and provides a line of defense for construction workers.

Existing RGB + depth (RGB-D) salient object detection methods mainly focus on better integrating the cross-modal features of RGB images and depth maps. Many methods use the same feature interaction module to fuse RGB and depth maps, which ignores the inherent properties of different modalities. In contrast to previous methods, this paper proposes a novel RGB-D salient object detection method that uses a depth-feature guide cross-modal fusion module based on the properties of RGB and depth maps. First, a depth-feature guide cross-modal fusion module is designed using coordinate attention to utilize the simple data representation capability of depth maps effectively. Second, a dense decoder guidance module is proposed to recover the spatial details of salient objects. Furthermore, a context-aware content module is proposed to extract rich context information, which can predict multiple objects more completely. Experimental results on six benchmark public datasets demonstrate that, compared with 15 mainstream convolutional neural network detection methods, the saliency map edge contours detected by the proposed model have better continuity and the spatial structure details are clearer. Perfect results are achieved on four quantitative evaluation metrics. Furthermore, the effectiveness of the three proposed modules is verified through ablation experiments.

There is an “Infodemic” of COVID-19 in which there are a lot of rumours and information disorders spreading rapidly, the purpose of the study is to build a predictive model for identifying whether the COVID-19 information in the Malay language in Malaysia is real or fake. Under the study of COVID-19 fake news detection, the synthetic minority oversampling technique (SMOTE) is used to generate synthetic instances of real news in the training set after natural language processing (NLP) and before data modelling because the number of fake news is approximately three times greater than that of real news. Logistic regression, Naïve Bayes, decision trees, support vector machines, random forests, and gradient boosting are employed and compared to determine the most suitable predictive model. In short, the gradient-boosting classifier model has the highest value of accuracy and F1-score.

At present, the state’s financial aid for students from financially disadvantaged families is increasing, and the number of students receiving financial aid is also increasing substantially. Poor students have to run back and forth to submit various materials while busy with their own studies, which causes a lot of inconvenience to students and teachers. The development of the poverty-stricken student precision funding platform frees teachers from tedious and low-end work and engages in core affairs, which improves the overall efficiency of school affairs and the controllability of information. In this context, this paper uses computer intelligence technology to realize the construction of the precise funding platform for impoverished students in colleges and universities (CAU) and completes the following tasks: (1) the research status of precise funding for impoverished students in CAU at home and abroad is introduced. (2) The SSI frame structure diagram of the funding information management platform is designed and the specific function description and detailed design of the relevant modules are given. (3) The module function test and system function test of the platform designed in this paper are completed through experiments. The results show that the system is very stable, and the functions are perfectly realized.

With the rapid development of 5G network and information technology, the means of modern multimedia are becoming more and more perfect. Many enterprises and industry organizations have entered the information age, and the field of higher education cannot lag behind because of its practical importance of training talents. The major of Chinese language and literature has a great influence on the development of the country’s soft power. The current teaching mode is backward and old-fashioned, paying more attention to the examination-oriented education of theoretical knowledge, and the practical training of comprehensive quality is still insufficient. This paper plans to design an intelligent classroom teaching system. Under the intelligent control of multimedia, the system can assist teachers to formulate flexible and rich teaching programs of Chinese language and literature. Finally, the weight distribution of the teaching evaluation system designed in this paper is reasonable, and the material library of the system is set up scientifically and comprehensively. After several groups of simulation verification, the improved teaching scheme helped the students who participated in the experiment to improve their academic performance by nearly 30 points. While maintaining the interest in Chinese language, the accuracy of spam article recognition algorithm is more than 85%, and the highest result can reach 94.5%.