Image binarization method based on accumulative edge point visual gray range histogram

Abstract

The invention discloses an image binarization method based on an accumulative edge point visual gray range histogram. A global threshold method is provided, the global threshold method comprises the following steps of: performing edge detection on an input gray image; initializing and assigning 0 to statistic values of all gray levels; obtaining the accumulative edge point visual gray range histogram; taking the gray level corresponding to a peak value of the histogram as the optimal threshold of the image binarization; and obtaining the binarized image according to the optimal threshold. A local adaptive threshold method is also provided, and the local adaptive threshold method comprises the following steps of: dividing the input image into n*m equal sub-regions; performing edge detection on the divided image; obtaining the global optimal binarization threshold by the global threshold method, and measuring the histogram; setting a binarization threshold; obtaining the binarized imageaccording to the optimal threshold. The method provided by the invention can maximally keep important transition information of the image, greatly reduce computation complexity of the algorithm and significantly improve efficiency of the algorithm, so that the entire image can present satisfactory visual effect.

Description

technical field [0001] The invention relates to an image binarization method. In particular, it relates to an image binarization method based on the histogram of the visible gray range of accumulated edge points. Background technique [0002] With the development of high-speed computers and large-scale integrated circuits, digital image processing technology has made a series of gratifying breakthroughs and progress, and its results are widely used in many fields such as biomedical engineering, industrial manufacturing, space exploration, public security, culture and art, etc. . However, many problems still exist in the digital image processing technology itself, hindering its further development and popularization. Among them, the problem of large amount of data is a major problem faced by image processing technology at present. Since the image records scene information in the form of a pixel array, and the quantization accuracy of each pixel generally reaches 256 levels...

AI Technical Summary

Problems solved by technology

The local adaptive threshold algorithm can better overcome the above problems. Taking the Niblack algorithm as an example, this method calculates the average gray value and variance of each pixel's local neighborhood point by point to determine the threshold of each pixel position, which is suitable for background or lighting conditions Complex images, but the algorithm has a large computational complexity, takes a long time and has obvious noise problems

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