基于提升改进方向波变换的浮选泡沫图像降噪方法

Flotation froth image de-noising algorithm based on lifting improved directionlet transform

针对矿物浮选过程中泡沫图像易受噪声影响,存在纹理细节模糊、灰度值对比度低等问题,提出一种浮选泡沫图像的非线性降噪方法。首先构造一种改进方向波变换,保证信号的平移不变性,同时采用提升算法减小其运算量。然后通过对分解系数建模,针对低频子带系数采用多尺度Retinex算法进行处理,以改善整体亮度均匀性,提高对比度;对各高通子带构建基于高斯混合尺度模型的分解系数邻域模型,并利用Bayes最小均方(BLS)估计进行局部去噪。最后利用所提出的方法对大量浮选泡沫图像进行去噪分析。结果表明:所提出的降噪方法能突出泡沫图像的纹理细节信息,提高泡沫图像的对比度,在信噪比和实时性上有明显提高,为后续泡沫图像的分割和工况识别奠定基础。

Considering the defects,such as easy sensitivity to noise and heavy texture,low contrast of gray value in the process of the floatation of foam image,a non-linear de-noising method was proposed. Lifting improved directionlet transform was firstly constructed,which not only ensured the shifting invariance but reduced its complexity. Multi-scale Retinex algorithm dealing with low-frequency subband coefficient was proposed for improving luminance uniformity and overall contrast. For high-pass subband,a model of decomposition coefficients neighbourhood based on Gaussian scale mixtures model was proposed for de-noising the image locally using Bayes least square（BLS）. The analysis on the effect of de-noising was given to lots of real froth images. The results show that the proposed method is successful in maintaining edges and is superior in de-noising in term of PSNR and visual effect. It lays a foundation for foamy segmentation and analyzing grade from flotation froth image.