Color image automatic focusing method

Abstract

The invention discloses a color image automatic focusing method. The method comprises color image antagorism graph obtaining, focusing sampling point acquisition based on a focusing window and a sharpness evaluation function of improved Brenner. According to the method, first of all, by full use of color information of a color image, loss of color details, caused by evaluation of sharpness only by use of a gray-scale gap; secondly, through comprehensive consideration of the feature that a main body target is often disposed at the center of a field of view and importance of a golden section ratio, the increased computational complexity due to statistics of the color information and the negative influence of a non-target area are reduced, and the real-time performance and the accuracy of focusing are effectively improved; and finally, through an improved Brenner function, under the condition that the computational complexity is not increased, the sharpness of the whole image is more comprehensively reflected.

Description

technical field [0001] The invention belongs to the technical field of image processing, and more specifically relates to an automatic focusing method for a color image. Background technique [0002] With the development of instrument automation and intelligence, image-based autofocus has become a key technology for imaging systems such as cameras, video cameras, and microscopes. The key to realizing image-based autofocus technology is to construct a fast and reliable sharpness evaluation function, and the position corresponding to the extremum of the function is the focus position of imaging. Since the image sharpness evaluation function needs to evaluate the defocus amount of a large number of images in the complete process of the imaging system gradually approaching the focus position to just at the focus position and then away from the focus position, a good image sharpness function In addition to having unbiasedness, unimodality, sharpness and noise resistance, it is a...

AI Technical Summary

Problems solved by technology

[0003] In order to reduce the amount of calculation, the existing sharpness evaluation functions are only calculated for the grayscale image, resulting in the loss of a large amount of color information

However, in a large number of actual scene images such as biomedicine, the color information of the target is very important, and the loss of color information may lead to the inability to obtain the best focus position; and if the sharpness evaluation function is calculated for the RGB three color channel images, it is difficult to Avoid the problem of greatly increasing the amount of calculation

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