针对基于图像的煤矸分选中，由于生产环境中粉尘、水雾、电磁干扰以及光源设备等因素， 引起图像照度变化、影响图像质量、降低识别率的问题，提出一种基于快速引导滤波的 Retinex 算 法，通过去除原始照度分量，添加合适的照度分量，实现图像整体亮度的调节。 采用 2 500，4 000， 5 500，7 000 lux 四种照度模拟实际工况环境照度的变化，并采集韩城矿区的瘦煤和页岩，铜川陈家 山矿区的弱黏煤和粉砂岩不同类型的煤和矸石图像，建立图像样本库。 对获取的图像通过添加0.1 为间隔、0.1~0.9的光照因子进行图像强化。 选取煤和矸石图像具有较大差异的标准差和熵2个 特征参数，分析增强前后煤和矸石图像 2 个特征参数变化规律以及 2 者标准差差值和熵差值的变 化规律。 将标准差差值和熵差值进行融合归一化，以最大差值法得到 2 个矿区 4 种照度下对应最 佳的光照因子。 以 LSSVM 为分类器，标准差和熵为输入向量，进行训练和识别验证。 结果显示:采 用最佳光照因子图像增强后，2 500，4 000，5 500，7 000 lux 四种照度下，韩城矿区样本相对未增强 前识别率分别增加了 7.5%，8.0%，8.5%，2.0%，陈家山矿区样本识别率分别增加了 0.5%，12.0%， 17.0%，25.0%，每张图像增强时间平均为 0.031 s。

Image⁃based coal and gangue separation is susceptible to dust，water mist，electromagnetic interference and light produced by equipment，whose factors change the environmental illuminance and further decrease image quality. This paper solves this problem by proposing a Retinex algorithm based on fast⁃guided filtering，which adjusts the brightness of the images by replacing the original illuminance component with a more suitable one. Firstly，this paper builds an image dataset consisting of lean coal and shale from Hancheng city and weakly caking coal and siltstone from Chenjia mountain mining area，and simulates the environmental illuminance of the actual working conditions by using four illuminances including 2 500，4 000，5 500 and 7 000 lux. Secondly，as the images are enhanced by adding a light factor of 0.1 with the interval in the scope of 0.1-0.9，this paper analyzes how the standard⁃deviation and entropy of the images change before and after the enhancing，as well as the difference of standard⁃deviation and the difference of entropy between coal and gangue images. After normalizing the difference of the standard deviation and the differ⁃ ence of entropy， the best light factor corresponding to the four illuminances is obtained by the maximum differ⁃ ence method. Using LSSVM as the classifier，trained and validated using entropy and standard deviation as the input vectors，the results show that the image recognition rate using the best light factor for image enhancing under the four illuminances is increased by 7.5%，8.0%，8.5%，2.0% respectively in the samples from Hancheng City，and 0.5%， 12.0%，17.0%，25.0% respectively in the samples from Chenjia mountain. The average time of image enhancement is 0.031 s.