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.

中文翻译：

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基于区域分解和引导滤波的浮选泡沫图像增强


从泡沫图像中提取信息对于泡沫浮选的自动控制非常重要。然而，相机捕获的图像经常遭受严重不均匀的照明，这显着降低了泡沫图像的质量。低质量的图像阻碍了泡沫信息的准确提取，从而影响泡沫浮选系统的控制。因此，我们提出了一种基于区域分解和引导滤波的图像增强方法来提高图像质量。最初，我们根据反射率将图像分为照明充足和不足的区域。在照明不足的区域，引导滤波器应用于直接像素以从其邻近的较亮点获取信息。相反，在其他区域，我们调节像素变化的幅度以防止过度曝光。最后，提出了一种基于多尺度高斯金字塔和纹理融合的细节增强方法，以提高清晰度和自然度。实验表明，我们提出的方法超越了公共数据集上的几种最先进的算法。在浮选领域，我们的方法有效地提高了图像质量。与相同分割策略下的其他增强方法相比，我们的方法显着提高了分割精度，展示了其强大的实用价值。此外，我们的方法在计算速度方面也显示出优势。