Image noise reduction method based on region growth label

Abstract

The invention discloses an image noise reduction method based on a region growth label. The method comprises the following steps: S1, searching and determining a seed point; S2, carrying out regionalgrowth, marking seed points, carrying out stack-in growth, judging whether a stack top pointer is greater than -1 , if so, entering the next cycle process, carrying out stack-out on the grown seed points, carrying out statistics on the number of the seed points after stack-out, and judging whether eight field points of the seed points meet growth conditions; S3, continuing to traverse the image, judging whether the pixel points which do not grow meet the growth condition, continuously searching the field points meeting the growth condition in the eight field points and combining the field points, and labeling the seed points belonging to different regions until the image traversal is finished; and S4, setting the pixel value of the image region of which the total pixel value is lower thanthe threshold value to be 0 according to the set threshold value requirement on the total pixel value of one region, namely, taking the image region as noise, removing the noise, and setting the pixelvalues of other regions to be 255, namely, an effective region.

Description

technical field [0001] The invention relates to the technical field of image processing, in particular to an image noise reduction method based on region growing labels. Background technique [0002] Due to its intuitive and clear features, binary images are widely used in target detection, location extraction and other fields, and faxes and commercial scanned documents are binary images. However, due to factors such as shooting environment, equipment limitations, poor image preprocessing and edge detection, the final extracted binary image often has various types of noise, which greatly reduces the quality of the image and affects its use. At the same time, due to its low entropy characteristics, many classic noise reduction methods in image processing, such as Gaussian filtering and mean filtering, are difficult to be applied to binary images. [0003] Aiming at the problem of noise in binary images, there is currently no more classic noise reduction method. The most comm...

AI Technical Summary

Problems solved by technology

However, due to factors such as shooting environment, equipment limitations, poor image preprocessing and edge detection, the final extracted binary image often has various types of noise, which greatly reduces the quality of the image and affects the use of

At the same time, due to its low entropy characteristics, many classic noise reduction methods in image processing, such as Gaussian filtering and mean filtering, are difficult to apply to binary images.

[0003] Aiming at the problem of noise in binary images, there is currently no more classic noise reduction method. The most commonly used method is to use morphological image processing, including multiple expansion and erosion operations, or open and close operations on binary images.

Although these simple methods can reduce the noise of binary images to a certain extent, they often damage a large amount of effective information on the image, and the gain outweighs the gain.

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