Images captured in hazy environments experience gradient reduction due to the scattering of light by atmospheric particles, yet few existing dehazing methods utilize the gradients of hazy images to restore the visibility of the scene. In this paper, we propose an efficient dehazing method based on gradient line prior. By associating image gradients in atmospheric scattering model, we obtain a stable and simple prior, termed the gradient line prior (GLP), that is, in the corresponding hazy images normalized by atmospheric light, there is a linear relationship between the reciprocal of the gradients distributed across different regions of the hazy image plane and the corresponding ratio of pixel intensity to the gradient. For accurate estimation of the transmission map, its refinement is realized through the analysis of the distribution characteristics of pixels in three channels. Experimental results demonstrate that, compared to different dehazing methods, our approach exhibits good detail enhancement and color restoration performance. The code is available at: https://github.com/heyuanlei/Gradient-Line-Prior.

Froth velocity is one of the key control indices affecting the performance of froth flotation. In the flotation production process, the froth velocity is usually set based on manual experience, which is subjective and arbitrary, which is highly subjective. Moreover, the relationship between the flotation process characteristics and froth velocity is complex, making it difficult to optimize the froth velocity setting. Therefore, an intelligent optimization setting method for froth velocity that integrates multi-source flotation operation information is proposed. In this study, we first analyze the relationship between different operational conditions and the flotation speed, establishing a model for the flotation speed. Subsequently, we employ case-based reasoning (CBR), combined with a flotation speed evaluation and learning model, to obtain the set values for the flotation speed. Finally, an intelligent compensation prediction model based on extreme trees regression (ETR) is utilized to derive the final predicted flotation speed values. Throughout these steps, a flotation production index prediction model is applied to forecast various flotation production indices under the predicted flotation speed. Experiments utilizing historical process data from a flotation production experimental platform demonstrated that the froth velocity settings derived from this method resulted in reduced fluctuation in production indicators and a 12% increase in the comprehensive qualification rate of flotation process indicators. Additionally, the concentrate output rose by 13.9%. This approach significantly enhances flotation production conditions and key performance indicators, confirming its practical application value.

The absorption and scattering of light in different turbid media cause images to suffer from poor visibility and contrast, which severely affects the performance of many computer vision tasks. To address this issue, we propose a fast scene recovery method based on the Ambient light similarity prior (ALSP). In this method, the ambient light similarity metric is designed from both magnitude and orientation, which is embedded into the optical imaging model, and the estimation of scene transmission is derived by simplification and approximation. The estimation of the transmission map is very simple, and its time complexity is O(N), where N is the size of the input image. Moreover, we propose a progressive manner to determine the ambient light for both the near and far regions separately, which can effectively improve the brightness and color saturation of the restored image. Experiments performed in different scenes demonstrate that our method outperforms several state-of-the-art competitors in terms of efficiency and scene recovery performance.

The traditional malicious uniform resource locator (URL) detection method excessively relies on the matching rules formulated by the network security personnel, which is hard to fully express the text information of the URL. Thus, an improved multilayer recurrent convolutional neural network model based on the YOLO algorithm is proposed to detect malicious URL in this paper. First, single characters are mapped to dense vectors using word embedding, and the dense vectors are participated in the training process of the whole model according to the structural characteristics of the URL in the method. Then, the CSPDarknet neural network model based on the improved YOLO algorithm is proposed to extract features of the URL. Finally, the extracted features are used to evaluate malicious URL by the bidirectional LSTM recurrent neural network algorithm. In order to verify the validity of the algorithm, a total of 200,000 URLs are collected, including 100,000 normal URLs labeled “good” and 100,000 malicious URLs labeled “bad”. The experimental results show that the method detects malicious URLs more quickly and effectively and has high accuracy, high recall rate, and high accuracy compared with Text-RCNN, BRNN, and other models.