Abstract
Froth image could strongly indicate the production status in mineral flotation process. Considering low contrast and sensitivity to noises and illumination of froth images in flotation cells, an improved image enhancement algorithm based on nonsubsampled contourlet transform (NSCT) and multiscale Retinex algorithm has been proposed. Nonsubsampled contourlet transform was firstly adopted to decompose the flotation froth images, ensure signals invariance and avoid the blurring edge. Secondly, a multiscale Retinex algorithm was used to enhance the lower frequency image and improve the brightness uniformity. Adaptive classification method based on Bayes atrophy threshold was proposed to eliminate noise, preserve strong edges, and enhance weak edges of band-pass sub-band images. Experiment shows that the proposed method could enhance the edge, contour, details and curb noise, and improve visual effects. Under-segmentation caused by noise and blurring edge has been solved, which lays a foundation for extracting foamy morphological flotation froth and analyzing grade.
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