泡沫图像统计建模与恒常颜色校正算法研究
The statistical modeling and constant color correction algorithms for froth image

由于浮选现场的恶劣环境和复杂光照等问题, 将导致采集到的泡沫图像不可避免地发生色偏. 为了消除以 上不利影响, 提高后续颜色特征提取的准确性, 本文提出了一种基于图像统计建模的泡沫图像恒常颜色校正算法. 首先, 通过对泡沫图像统计特性的分析, 利用Contourlet变换和广义高斯分布函数对图像进行统计建模. 其次, 选取 颜色恒常性标准图像库(Gray-ball)中已知真实光照的标准图像作为训练样本, 建立其统计模型参数集, 分别选用常 用的5种颜色恒常性算法对其进行颜色校正, 并以最小角度误差为每幅标准图像标记最佳颜色恒常性算法. 最后, 利用K最近邻分类算法将待校正的泡沫图像分至对应的恒常算法中, 该算法即为原始泡沫图像的最佳恒常颜色校 正算法. 实验结果证明, 该算法能够获得较好的颜色校正效果.
Due to the bad environment and the complex illumination of the flotation scene, the froth image always suffers serious noise and color cast. In order to eliminate the above adverse effects and improve the veracity of color feature extraction, color correction algorithms for froth image is proposed based on image statistical modeling theory in this paper. At first, through the analysis of the statistical characteristics of froth image, Contourlet transform and generalized Gaussian distribution are used to build statistical model of image. Next, pictures with known real light in standard picture library of color constancy (Gray-ball) are chose as the training samples and their statistical parameters are calculated to compose a statistical model parameter set. Five common color constancy algorithms are selected to do color correction and the minimum angle error is used to mark the best color constancy algorithm of each picture. In the end, the best color constancy algorithm of original froth image is achieved by K–NN (K–nearest neighbor classification). The experimental results shows that the algorithm can obtain a good color correction effect for froth image.