Flotation froth image enhancement based on region decomposition and guided filtering.

• We propose an image enhancement method based on region decomposition and guided filtering. • In regions with insufficient illumination, we employ guided filtering to get information from brighter points in their neighborhood. • In regions with sufficient illumination, we regulate the magnitude of pixel variations to prevent overexposure. • A detail enhancement method is proposed based on a multi-scale Gaussian pyramid and texture fusion to improve clarity and naturalness. • The experiments show that the proposed method surpasses several state-of-the-art algorithms. Extraction of information from froth images is important for automatic control of froth flotation. However, images captured by cameras often suffer from severe uneven lighting, which significantly reduces the quality of froth images. Low-quality images hinder the accurate extraction of froth information, thereby affecting the control of the froth flotation system. Hence, we propose an image enhancement method based on region decomposition and guided filtering to improve the quality of images. Initially, we separate the image into regions with sufficient and insufficient illumination based on reflectance. In regions with insufficient illumination, the guided filter is applied to direct pixels to acquire information from brighter points in their neighborhood. Conversely, in other regions, we regulate the magnitude of pixel variations to prevent overexposure. Finally, a detail enhancement method is proposed based on a multi-scale Gaussian pyramid and texture fusion to improve clarity and naturalness. The experiments show that the method we proposed surpasses several state-of-the-art algorithms on public datasets. In the field of flotation, our method effectively enhances the image quality. Compared to other enhancement methods under the same segmentation strategy, our method significantly improves segmentation accuracy, demonstrating its strong practical value. In addition, our method also shows advantages in terms of computational speed. [ABSTRACT FROM AUTHOR]