This paper presents an improved genetic algorithm for the optimization of the structure element used in morphological open and closed filtering. Considering that the evaluation of the froth images of coal flotation is categorized as a no-reference image evaluation, in the optimization of the structural element, a denoising evaluation index of an improved information capacity was used as the adaptation degree function. In addition, this paper proposes the determination method of chromosome length in the structure element optimization algorithm. In the improved genetic algorithm, based on adaptive variation, the variation regulation factor and the mechanism of concentration adjustment are introduced. When compared to an optimization process of the structural element in froth image denoising using the genetic algorithm, the adaptive genetic algorithm, the improved genetic algorithm improves the efficiency and accuracy of the optimization process. It has been proven that optimizing the structural element by the improved genetic algorithm increases fitness and reduces noise when using morphological open and closed reconstruction filters.
A new froth image classification method based on the MRMR-SSGMM hybrid model for recognition of reagent dosage condition in the coal flotation process