针对浮选槽低照度环境下采集的泡沫图像对比度低、边缘弱、噪声干扰等问题， 提出了一种结合自适应分数阶微分和非下采样 Contourlet 变换( NSCT) 的泡沫图像多尺度 增强算法． 首先对泡沫图像进行 NSCT 多尺度分解，根据低频子带的梯度特征构造自适应 分数阶微分阶次函数，结合改进的带亮度控制参数的 Tiansi 算子对低频子带图像进行增 强处理; 然后对各高频方向子带，根据能量分布特征自适应计算阈值，再结合尺度相关系 数去除噪声，并通过非线性增益函数增强边缘系数; 最后对处理后的图像进行 NSCT 重 构． 对不同大小类型的泡沫图像进行实验，结果表明: 与现有算法相比，文中算法改善了图 像的亮度，具有更高的对比度、清晰度和信息熵，保留更多的纹理细节，在有效抑制噪声的 同时气泡边缘得到明显增强，为后续的泡沫图像分割和边缘检测奠定了基础． 

 Considering the problems of low grey contrast，weak edges and noise interference for the foam image captured by the situation of the poor light in the floatation process，here is proposed a new foam image multiscale enhancement algorithm based on adaptive fractional differential and nonsubsampled contourlet transform (NSCT)． Firstly， foam image is decomposed through NSCT， the low frequency sub-band image is enhanced by combining the adaptive fractional differential order function constructed on the basis of pixel gradient feature，with the Tiansi operator which is improved by a set of brightness control parameters． Secondly，for high frequency sub-bands，threshold is adaptively computed according to the energy distribution，which is proposed to eliminate noise with the scale correlation coefficient，and the edge coefficients are enhanced by a nonlinear enhancement function． Finally， the processed image is reconstructed through NSCT． Enhancement experiment is performed by different kinds of bubble size images． Experimental results show that，compared to other current algorithms，the proposed algorithm is superior in improving the lightness，contrast，definition and comentropy of the foam image，and is successful in maintaining more texture details，which can also achieve much better performance to enhance bubble edges while removing the image noise． It lays a foundation for the subsequent foam image segmentation and edge detection．