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.

Continuous and accurate depth information of blast furnace burden surface is important for optimizing charging operations, thereby reducing its energy consumption and CO2emissions. However, depth estimation for a single image is challenging, especially when estimating the depth of burden surface images in the harsh internal environment of the blast furnace. In this paper, a novel method that is based on edge defocus tracking is proposed to estimate the depth of burden surface images with different morphological characteristics. First, an endoscopic video acquisition system is designed, key frames of burden surface video in stable state are extracted based on feature point optical flow method, and the sparse depth is estimated by using the defocus-based method. Next, the burden surface image is divided into four subregions according to the distribution characteristics of the burden surface, the edge line trajectories and an eight-direction depth gradient template are designed to develop depth propagation rules. Finally, the depth is propagated from edge to the entire image based on edge line tracking method. The experimental results show that the proposed method can accurately and efficiently estimate the depth of the burden surface and provide key data support for optimizing the operation of blast furnace.

An industrial endoscope can monitor the abnormal conditions of the blast furnace burden layer by detecting the burden surface. Aiming at the problem of a small area of burden surface detected by industrial endoscopes due to the harsh environment of high temperature in blast furnaces, a novel optimization method of the installation direction of industrial endoscopes is proposed. First, a modeling method based on the three-dimensional coordinate transformation was proposed to quantitatively describe the relationship between the installation direction and imaged burden surface area. Then, we designed the optimization program based on particle swarm optimization to maximize the imaged burden surface area. Finally, the problems existing in field applications were considered, and the corresponding model of imaged burden surface area was established for the application. Experimental results show that the proposed method can effectively increase the imaged burden surface area, which provides a guarantee for the stable operation of blast furnaces.