Noise removal from image data based on mathematical morphology and genetic optimization

Considering the details losing in image filtering, an adaptive multiscale morphological filtering (AMMF) method is presented. The proposed method adds the multiscale top-hat transformation and bottom-hat transformation to the conventional multiscale morphological opening and closing filtering. The two added transformations are used to extract and smooth the features which are smaller than the current scale. The coefficients of the multiscale top-hat transformation and bottom-hat transformation have great effects on the performance of the whole filtering. So, they are optimized by aid of the genetic optimization method. Experiment results demonstrate that the AMMF method can remove noise effectively and preserve the details of images completely. It promotes the adaptability and intelligence of the filtering, and manifests better performances than the conventional filtering methods.