Colorful morphological image processing method based on fuzzy similarity

Abstract

The invention provides a colorful morphological image processing method based on fuzzy similarity and relates to the image processing technology field. The method comprises steps that to-be-processed colorful images are made to correspond to the RGB colorful space, a fuzzy similarity measurement (FSM) function used for representing the similarity degree of two colorful vectors is determined, a pixel is taken as an index, structure units and a colorful vector set of the structure units are acquired in the RGB colorful space one by one, a least upper bound vector and a greatest lower bound vector of the colorful vector set are determined based on the FSM criterion, colorful morphological basic operation is constructed, and application to colorful images is further carried out. The method is advantaged in that the morphological idea is applied to colorful image processing, good stability and strong practicality are realized in actual colorful image processing, not only can colorful target smoothing be realized, but also the detail characteristic of homogenous region pixel inconsistency can be further excellently processed, and colorful image analysis and processing targets can be finally realized.

Description

technical field [0001] The invention relates to the technical field of image processing, in particular to a color morphology image processing method based on fuzzy similarity. Background technique [0002] The basic concept of morphology first appeared in the 19th century. Applying morphology to the field of image processing is to use structural elements (circles, squares, line segments, etc.) to extract and collect image information to analyze and identify the geometric features and structure. As a nonlinear image processing and analysis method, mathematical morphology has been successfully applied to binary and grayscale images, and a complete morphology theory has been formed. Among them, the binary morphology regards the binary image as a set, and uses the simplest set operation (such as intersection, union, complement and translation, etc.) to detect the original image. Since the operations used are all based on sets, binary morphology has the characteristics of clear...

AI Technical Summary

Problems solved by technology

Another way to extend grayscale morphology to color morphology is to sort the colors in structural units based on multivariate data sorting criteria, such as edge order, conditional order, region order, and degenerate order, and use similar grayscale morphology The principle of minimum and maximum defines basic morphological operations, but it is difficult to find a suitable and general color vector sorting method for colorful color images, so the current color morphological methods have great limitations

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