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

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.