Image enhancement method implemented through histogram equalization

Abstract

The invention discloses an image enhancement method implemented through histogram equalization. The method comprises the following steps: a digital image required to be enhanced is input into the MATLAB software so as to obtain a matrix expression f (m, n); then, the average brightness value Y of the image f (m, n) is calculated, and the maximum value Kmax and minimum value Kmin of gray values K of pixels in all pixels of the image f (m, n) are calculated; according to the Kmax, the Kmin and the Y, an original image is divided into two subimages in gray intervals [Kmin, Y] and [Y, Kmax], and the average brightness values Y1 and Y2 of the two subimages in the gray intervals of [Kmin, Y] and [Y, Kmax] are calculated, and finally, the original image is divided into subimages in gray intervals [Kmin, Y1], [Y1, Y], [Y, Y2] and [Y2, Kmax]; and according to the gray value K of each pixel in the original image f (m, n), carrying out histogram equalization through a histogram equalization formula of a gray interval at which the pixel is located; and then a brightness preservation based enhanced image is output, wherein the output enhanced image is good in effect.

Description

technical field [0001] The invention relates to an image enhancement technology, which belongs to the field of image processing, and in particular to an image enhancement method based on brightness preservation based on multi-sided histogram equalization. Background technique [0002] The purpose of image enhancement is to improve the visual effect of the image, so as to provide an intuitive, clear and suitable image for analysis. Among many image enhancement methods, histogram equalization is one of the classic and effective image enhancement methods. Based on probability theory, it uses a grayscale transformation function to modify the histogram of the input image so that it tends to be evenly distributed, so as to increase the dynamic range of the grayscale of the image, so as to achieve the purpose of image enhancement. [0003] Although histogram equalization has many advantages such as fast operation speed and obvious enhancement effect, there are still some defects: ...

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Problems solved by technology

[0003] Although histogram equalization has many advantages such as fast operation speed and obvious enhancement effect, there are still some defects: (1) Although the gray distribution histogram of the output image is close to uniform distribution, there is still a gap between the actual value and the ideal value. There may be large differences, not the best value

(2) When the quality of the original image is relatively poor, the gray dynamic range is small, and the histogram distribution is extremely uneven, the layered image transformed by the traditional histogram equalization operation is even worse

[0004] For an image in the [0255] grayscale range, after histogram equalization and enhancement, the image will appear over-enhanced (that is, bright pixels will be brighter after transformation, and dark pixels will be darker after transformation), the image The basic characteristics such as the average brightness change and the loss of details affect the visual effect of the enhanced image, so the application range of traditional histogram equalization is limited

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