Fast noise-containing image two-dimensional maximum between-class variance threshold value method

Abstract

The invention relates to a fast noise-containing image two-dimensional maximum between-class variance threshold value method, which comprises the steps of firstly solving a gray average value and a gray standard deviation of a noise image; smoothing each pixel of the image by adopting an average gray value of a 3*3 neighborhood to acquire a smooth image; then calculating the between-class variance of the smooth image by using a maximum between-class variance threshold value method, reducing the search space of a solution of the between-class variance through the gray average value and the standard deviation, traversing the search space, and recording a solution, which enables the between-class variance to be the maximum, to be an optimal one-dimensional threshold value T0; and calculating a trace of a between-class variance dispersion matrix of a target class and a background class by using a two-dimensional maximum between-class variance method, reducing the search space of a solution of the trace through the optimal one-dimensional threshold value T0 and the gray standard deviation of the noise image, traversing the search space of the solution, and recording a gray value binary group, which enables the trace of the dispersion matrix to be the maximum, to be an optimal two-dimensional cutting threshold value. The method provided by the invention can avoid traversal for all gray levels, and also can acquire an accurate solution while greatly reducing the calculation amount.

Description

technical field [0001] The invention belongs to the field of digital image processing, and in particular relates to a fast noise-containing image two-dimensional maximum inter-class variance threshold method in image threshold segmentation. Background technique [0002] Image segmentation is a key step from image processing to image analysis. It refers to the technology and process of dividing the image into regions with various characteristics and extracting the target of interest. By extracting the target of interest, a higher level of analysis and understanding is established. [0003] The existing image segmentation methods are mainly divided into the following categories: threshold-based segmentation methods, region-based segmentation methods, edge-based segmentation methods, and segmentation methods based on specific theories. Among them, the threshold-based image segmentation method is a widely used segmentation technology with relatively simple calculation and relat...

AI Technical Summary

Problems solved by technology

[0009] The purpose of the present invention is to propose a fast two-dimensional maximum inter-class variance threshold method for noisy images, which solves the problem of excessive computational complexity of the original two-dimensional maximum inter-class variance threshold method

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