Automatic focusing searching algorithm

Abstract

The invention relates to an automatic focusing searching algorithm, which combines a Hill Climbing searching method and a function approaching method. In the algorithm, the Hill Climbing searching method adopts a two-stage algorithm combining coarse and fine focusing: in the coarse focusing, a long pace selects a gradation variance function to fast approach to a focusing position considering the rapidity of the algorithm; and in the fine focusing, a shorter pace adopts a Laplacian function to accurately focus the position considering the sensitivity of the algorithm. A focusing interval is narrowed by comparing three images and an optimal focusing position is fitted using the function approaching method in small intervals. By using the method, image acquisition and assessment times are reduced, an automatic focusing duration of a system is shortened and the searching efficiency of the algorithm is improved; and the influence of a local peak can also be avoided by comparing evaluation function value of the three continuous images. An extreme point is provided by fitting analysis, which makes the extreme point more close to the position of the optimal focusing point, thus algorithm precision is improved significantly.

Description

technical field [0001] The invention relates to an image processing technology, in particular to an automatic focus search algorithm combining a hill-climbing search method and a function approximation method. Background technique [0002] The autofocus method based on the depth of focus method is easy to implement, and the search algorithm is the key technology of the depth of focus method. The traditional focus search algorithm needs to collect a large number of images for processing, which takes a long time and has low focus efficiency; and because the sampling rate and sampling starting point are different, the traditional search algorithm is likely to cause the obtained position with the global maximum focus evaluation function to be incorrect. The accuracy of the algorithm is relatively low for the problem of the best focus position. Contents of the invention [0003] The invention aims at the problems existing in the traditional auto-focus search algorithm, and pro...

AI Technical Summary

Problems solved by technology

The traditional focus search algorithm needs to collect a large number of images for processing, which takes a long time and has low focus efficiency; and because the sampling rate and sampling starting point are different, the traditional search algorithm is likely to cause the obtained position with the global maximum focus evaluation function to be incorrect. The best focus position of the problem, the accuracy of the algorithm is relatively low