Dynamic contrast ratio enhancement method based on function curve transformation

Abstract

The invention discloses a dynamic contrast ratio enhancement method based on function curve transformation, and relates to the technical field of image processing. The method comprises the following steps that a frame image in video is obtained; a gray-scale value of each pixel point in the frame image is obtained; global histogram statistics of the current frame image is established, a gray-scale value a1 is selected, a ratio n is set, the relation between the a1 and the n meets the condition that in the global histogram statistics, the ratio of the sum of the pixel points of which the gray-scale values range from 0 to the a1 to the total pixel points is n, or local histogram statistics of the current frame image is established, and a gray scale average a2 is calculated; the obtained gray-scale value of each pixel point is substituted into a formula, and a corrected gray-scale value of the pixel point is obtained; the gray-scale values of all the pixel points are modified to be I, and an enhanced frame image is obtained; a normalized ratio K is set, a ratio k of a gradient difference before and after adjustment of the current frame image is calculated, if abs(k-K) is smaller than or equal to m, processing of the frame image is completed, otherwise, coeff is adjusted, and the steps from third to fifth are repeated or the coeff is used in a formula of a next frame image.

Description

technical field [0001] The invention relates to the technical field of image processing, in particular to a dynamic contrast enhancement method based on function curve transformation. Background technique [0002] Image enhancement technology is a kind of image processing technology, which can significantly improve image quality, make image content more layered and subjective observation effect more in line with people's needs. [0003] Common image enhancement techniques include: saturation enhancement and contrast enhancement. Compared with saturation enhancement, contrast enhancement has received more attention. Contrast enhancement is to increase the distribution range of the gray scale of the image by adjusting the gray scale distribution of the image, so as to improve the contrast of the whole or part of the image and improve the visual effect. Contrast enhancement is further divided into: Histogram Equalization and Gamma Correction. The Gamma Correction method uses t...

AI Technical Summary

Problems solved by technology

[0004] At present, the dynamic contrast enhancement algorithm is mainly classified into two categories. One is to enhance the local details of the image, such as adaptive or local histogram equalization. This type of method works well for specific areas, but does not work well for the overall image.

The other is to enhance the global information of the image. To achieve the desired effect, this method often requires a large amount of storage space as a frame buffer. The real-time performance is poor, the calculation is complicated, and it is difficult to transplant to the device.

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