Focusing method and focusing device

Abstract

The invention provides a focusing method and a focusing device and belongs to the video monitoring technology field. The method comprises steps that a preparatory focus area is acquired according to focusing point clear values of images of multiple detection positions acquired by an image acquisition device; if a change state of the focusing point clear values of the preparatory focus area is abnormal, a next preparatory focus area and a focusing point clear value change state of the next preparatory focus area are acquired till the focusing point clear value change state of the next preparatory focus area is normal; a standard focus position is acquired for the normal preparatory focus area according to the focusing point clear value change state; the image acquisition device is controlled for moving to a detection position corresponding to the standard focus position. According to the method, through determining whether the focusing point clear value change state of the preparatory focus area is normal, falling into a local extremum can be avoided, search steps of the standard focus point position are reduced, a focusing process is greatly simplified, and focusing accuracy is improved.

Description

technical field [0001] The present invention relates to the technical field of video monitoring, in particular to a focusing method and device. Background technique [0002] The existing automatic focusing technology of digital image processing mostly obtains the image sharpness evaluation value through the sharpness evaluation function, and based on this value combined with some maximum sharpness evaluation value search algorithms, finds the maximum sharpness evaluation value, that is, obtains the sharpness evaluation value. Image. Usually, in a general focusing scene, as the focusing lens approaches or moves away from the actual focal point, the image sharpness increases or decreases correspondingly, and the sharpness evaluation curve of this scene presents a good single-peak type and the evaluation values ​​on both sides of the peak are monotonous decrease. The hill-climbing search algorithm for this scene combines the appropriate search step size and end conditions to ...

AI Technical Summary

Problems solved by technology

[0003] However, in scenes with moving objects or moving light sources, low-light or low-contrast scenes, the sharpness evaluation curve will have more false peaks compared to the single-peak type of general scenes, causing the focus search algorithm to fall into local extreme value, causing out-of-focus to appear

In this case, relying only on the focus evaluation value to search for a clear focus point, it is easy to search for a local extremum or cause the focus effect to vibrate repeatedly, affecting the accuracy of the focus effect

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