Image gradient field double-interval equalization algorithm based on histogram probability correction

Abstract

In order to effectively enhance an image lacking texture details, the invention provides an image gradient field double-interval equalization algorithm based on histogram probability correction by extending a traditional gray histogram to a gradient histogram. According to the algorithm, the over-enhancement effect of global equalization is considered, traditional global equalization is improved,a gradient histogram is divided into an edge part and a non-edge part of an image, then the two parts are equalized respectively, and the method solves the problem of over-enhancement of traditional global equalization. Finally, according to the mapping rule of histogram equalization, the algorithm proposes a single threshold method correction probability before the respective equalization of thetwo intervals, the operation effectively avoids the phagocytic effect of histogram equalization, and the information entropy of the enhanced image is greatly improved compared with the original image.

Description

technical field [0001] The invention belongs to the technical field of digital image processing, in particular to an image gradient field double-interval equalization algorithm based on histogram probability correction. Background technique [0002] Histogram Equalization (HE) is a commonly used method for image enhancement. Its advantages are small time complexity and obvious enhancement effect. And there is a small probability that the contrast decreases in the area. In response to the above shortcomings, many scholars have proposed image enhancement algorithms based on histogram equalization in recent years. Most algorithms divide the original histogram or original image into multiple sub-blocks by setting a threshold and then equalize them separately, or directly change the distribution of the histogram to achieve The prescribed shape, so that there is a better effect after equalization. [0003] Image gradient is an important indicator of the quality of image texture ...

AI Technical Summary

Problems solved by technology

This type of algorithm allows the amplitude of the original gradient field to be evenly distributed in the original range through the histogram, and the equalization result is determined by the distribution of the original gradient field amplitude. However, at present, this type of algorithm does not consider the excessive range of equalization and the uneven probability distribution of gradient levels. Problem, after equalization, the output image has shortcomings such as gradient field over-enhancement and gradient level being swallowed

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