基于WCGAN的矿物浮选泡沫图像光照不变颜色提取

浮选泡沫表面颜色是选矿生产指标(精矿品位)最为快速便捷的直接指示器. 然而, 泡沫图像信号因受多种可变光照的交叉干扰而不可避免存在严重色偏, 导致浮选指标难以准确评估. 本文将传统的基于光照估计的图像颜色恒常问题转换为一种结构保持的图到图颜色(风格)转移问题, 提出一种基于Wasserstein距离的循环生成对抗网络(Wasserstein distance-based cycle generative adversarial network, WCGAN)用于泡沫图像光照不变颜色特征在线监测. 在标准颜色恒常数据集和实际的工业铝土矿浮选过程进行实验验证, 结果表明, WCGAN能有效实现各种未知光照条件下(色偏)图像到基准光照条件下的颜色转换, 转换速度快且具有模型在线更新功能. 与传统的基于生成对抗学习的颜色转换模型相比, WCGAN能更好地保持泡沫图像的轮廓和表面纹理等结构信息, 为基于机器视觉的矿物浮选过程生产指标的在线监测提供了有效的客观评价信息.

WCGAN-based Illumination-invariant Color Measuring of Mineral Flotation Froth Images

The surface color of flotation froth can be referred as an instant and direct production index (e.g., concentrate grade) of mineral flotation process. However, serious color deviations will inevitably exist in the collected froth images, due to the adverse interference of varying multi-source illuminations, resulting in great difficulties in achieving accurate evaluation results of production indexes. This paper formulates the illumination estimation task in traditional color constancy as a structure-preserved image-to-image color translation task. It presents a Wasserstein distance-based cycle generative adversarial network (WCGAN) for the illumination-invariant froth color feature measuring. Extensive validation and comparative experiments on benchmark color constancy datasets and an industrial flotation process demonstrate that WCGAN can effectively realize the color translation of froth images from various unknown illuminations to a canonical illumination, which has the merits of fast translation and online model updating. Compared with the traditional generative adversarial networks-based color translation models, WCGAN can maintain the structural features invariance, e.g., geometric and texture structures, of original images more effectively, providing effective and objective evaluation information for the on-line monitoring of production indices of mineral flotation process.