The blast furnace (BF) is the most energy-consuming and emission-intensive equipment in the steel industry. Burden surface images provide reliable and concrete information that is crucial for real-time monitoring, understanding burden distribution, and ensuring efficient and green operations. However, the high-temperature, enclosed, dusty, and low-light environment within the BF results in images captured by high-temperature industrial endoscopes that suffer from unevenly illumination, abnormal light and shadow, and blurriness, limiting their application. This paper proposes a method for high-definition reconstruction of BF surface images based on a spatiotemporal multi-domain guided generative model (STMGG). This method constructs a deep image prior (DIP) network with an encoder-decoder mechanism to decompose the original burden surface image into illumination and reflectance maps across multiple domains. For the illumination maps, a multi-frequency-attention fusion block (MFAFB) extracts extreme bright and dark features from the original image, calculates the appropriate fusion weights for the illumination maps of each domain, and synthesizes an optimal distributed total illumination map to accurately restore light and shadow. For the reflectance maps, global temporal and multi-spatial domain local structural texture information guidance based on wavelet transform is introduced, along with a structural texture entropy loss, to ensure the generated reflectance maps focus on both global contours and local details, thus reducing blur. The final high-brightness, high-definition reconstruction of the BF burden surface is achieved by merging the total illumination and reflectance maps. Experimental results demonstrate that STMGG significantly enhances the quality of BF burden surface images and exhibits strong adaptability in general scene images, highlighting its broad applicability and significant practical 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.

Edge preserving filter is the basis of many computational photography and image processing. This can be achieved by global optimization method or local filtering method. Generally, the filtering results of global optimization methods are better than that of local filtering methods, and local filtering methods usually run much faster than global optimization methods. In this paper, a globally optimized method called iterative self-guided image filter (isGIF) is extended based on the assumptions of the guided image filter (GIF), which can produce high-quality edge-preserving filtering results by using the input image itself as the guidance image. Some comparisons with other edge-aware filters are presented to show the advantages of our method. Extensive experiments demonstrate that our filter generates images with better visual quality, while reducing/avoiding halo artifacts in the final image, and the running time is competitive.

The purpose of texture smoothing is to preserve the prominent structure in the image while smoothing the salient texture. However, the existing methods are difficult to achieve a satisfactory balance between filtering out salient textures and preserving weak edge structures and small structures. To this end, we propose a structure-preserving texture smoothing method via scale-aware bilateral total variation. First, the joint bilateral filter is introduced to construct the window bilateral variation, and combined with the window total variation, a regularizer called the bilateral total variation is formed, which accurately quantifies the characteristics of texture and structure, to finely smooth salient textures while preserving weak edge structures and small structures. Subsequently, we proposed a scale-aware scheme to make the proposed regularizer more powerful in preserving small structures and adopted an optimization scheme to convert the original non-convex optimization problem into a least squares regression problem. The effectiveness of the proposed regularizer is verified in the dataset. The experimental results demonstrate the superiority of the proposed method in texture smoothing and other applications compared to other state-of-the-art approaches.

An optimal switching heating control strategy based on minimum margin (OS-MM) is proposed for high efficiency, low energy consumption, and high-uniformity fine control in a large-scale vertical quench furnace. This work was stimulated by the need for solving the contradiction between the heating rate and temperature overshoot to achieve a rapid rise in temperature with no overshoot. Based on a three-dimensional (3-D) transient temperature field model of the quenching furnace, the temperature field optimization control problem is transformed into a boundary optimization control problem of rapid heating, whereby it is rigorously proved that the fastest rise in temperature is attained by the full-power heating, which satisfies the bang-bang characteristics. The key parameter that determines the overshoot in the full-power heating mode is introduced and defined as the minimum margin in the temperature-rising process. The analytical expression for solving the minimum margin is calculated by the eigenfunction expansion method and using the strong metal thermal inertia of the internal temperature field, the OS-MM method is designed to stop the heating in advance at a calculated switching point to optimize full-power heating, thus conserving energy by reducing its consumption. The results of simulation and industrial experiments show that this strategy greatly shortens the temperature adjusting time, significantly reduces energy consumption, and effectively improves the control performance indices, such as overshoot and static error in the temperature holding period of the thermal treatment process, as compared to the existing heating methods.

Accurate temperature measurement of blast furnace molten iron is essential for regulating the furnace temperature. However, the dust interference at blast furnace cast field makes it difficult to measure molten iron temperature accurately using the infrared temperature measurement method. To reduce the influence of dust on the results of infrared measurement method, a compensation method using the heterogeneous features of infrared images is proposed in this article. First, the infrared image of the molten iron flow is divided into subregions, from which the subregions containing only molten iron are selected. Then, the statistic and the texture features influenced by dust are extracted from the selected subregions. Finally, the heterogeneous features are used as the compensation model input to estimate the measurement error of each subregion and compensate for the molten iron temperature. Experimental results demonstrate that the compensation method can significantly reduce the infrared measurement errors caused by dust and obtain accurate molten iron temperature.