Fuzzy clustering color image segmentation method based on multi-channel weighted guided filtering

Abstract

The invention discloses a fuzzy clustering color image segmentation method based on multichannel weighted guided filtering, and the method comprises the steps: firstly effectively removing different types of noises with different intensities through multivariate morphological reconstruction for a color image with noises, thereby reducing the impact on image clustering from the noises; secondly, when multi-channel guided filtering is carried out on the membership degree of the color image with the noise, guiding and filtering the membership degree by using each channel of the original color image; the filtering result of each channel is weighted to obtain the final filtering output image, so that the mutual influence between different channels can be avoided, the edge of the final filteringresult can be kept more effectively, and the accuracy of color noise image segmentation is improved. In addition, due to the fact that filtering result weighting calculation of the three channels islinear operation, the fuzzy clustering color image segmentation method based on multi-channel weighting guided filtering has low time complexity.

Description

technical field [0001] The invention relates to the technical field of color noise image segmentation, in particular to a fuzzy clustering color image segmentation method based on multi-channel weighted guided filtering. Background technique [0002] Image segmentation refers to dividing an image into several non-overlapping sub-regions, so that each sub-region has a certain similarity, while different sub-regions have obvious differences. Because color images provide richer information than grayscale images, color image processing has received more and more attention. Color image segmentation is an important issue in color image processing. However, in practical applications, the color image to be segmented often contains noise of unknown type and intensity, which leads to poor color image segmentation results. Therefore, for example, in the case of unknown noise type and intensity, improving the segmentation effect of color noise images has become a research hotspot. [...

AI Technical Summary

Problems solved by technology

However, in some cases, multivariate morphological reconstruction will change some details in the color image, resulting in misclassification of the local area of ​​the image.