Image enhancement method based on sine curve change and application thereof

Abstract

The invention discloses an image enhancement method based on sine curve change and application thereof, and the method comprises the following steps: obtaining an RGB color image, presetting a brightness increment parameter, and carrying out the sine normalization processing of the RGB color image, thereby obtaining a global sine normalization image; graying the RGB color image to obtain a grey-scale map, carrying out Sobel edge detection, respectively and independently carrying out convolution on the grey-scale map in the x direction and the y direction by adopting a transverse difference operator and a longitudinal difference operator, and carrying out weighted merging to obtain an edge image; and performing Gaussian convolution kernel filtering smoothing on the edge image, performing convolution on a Gaussian convolution kernel and the edge image, presetting a sharpening intensity adjustment parameter, performing exponentiation according to the sharpening intensity adjustment parameter to obtain an enhancement index, and performing exponentiation on the global sine normalized image and the enhancement index to obtain an enhanced image. According to the method, the image brightness increment is in normal distribution, so that smooth adjustment is realized, the image is sharpened to different degrees by adopting the edge sharpening weight, and a better image enhancement effect is obtained.

Description

technical field [0001] The invention relates to the technical field of image enhancement, in particular to an image enhancement method based on a sinusoidal curve change and its application. Background technique [0002] The existing linear enhancement method based on color space transforms the image into HSV or RGB space, performs linear mapping on the brightness value, and changes the brightness and clarity by adjusting the coefficients affecting brightness and contrast; the existing Gamma correction technology is A method of curve enhancement, which performs nonlinear mapping on the image, and the dynamic range of different pixel distribution areas will be changed to different degrees, so as to achieve the purpose of image contrast enhancement. There are many non-linear brightness adjustment methods, and the usual method is to design a reasonable transformation formula according to specific application standards to change the image brightness without losing contrast. [...

AI Technical Summary

Problems solved by technology

[0003] Existing image enhancement techniques have many deficiencies in adaptive adjustment. In most linear and nonlinear image enhancement methods, brightness and contrast are independent of each other, and the increment of each pixel is the same, resulting in a decrease in contrast. In the process of automatically adjusting contrast In , it is difficult to adjust an appropriate value. For example, under a given value range, adjust the average brightness of the image to this range. If linear adjustment is performed, all pixels will be increased by the same amount, and the brightness will be enhanced. At the same time, if If the contrast parameter is not changed, the contrast of the image will decrease, and the whole will become blurred. The contrast adjustment is uncontrollable under this artificially set automatic adjustment method, so this method is not practical enough

For the Gamma curve enhancement technology, different index Gamma is selected to obtain different transformation curves. Although this adjustment method can adjust the brightness and enhance the contrast in different areas to different degrees, the change is still relatively drastic, and it is easy to cause the original image to be damaged in practical applications. Distortion, it is difficult to get a more balanced and natural effect

Images