Iterative optimization of auto-focusing method based on hill-climbing method

Abstract

The invention discloses an iterative optimization automatic focusing method based on a hill-climbing method. Through the iterative optimization algorithm based on the hill-climbing method, an image corresponding to the highest definition evaluation value in an image database and its serial number are calculated, and finally a stepping motor is driven to move. Focusing is achieved at the position corresponding to this number, that is, the focusing position, and the mathematical model is established to prove that the calculation time of the image clarity evaluation of the focusing method proposed in the present invention is greater than the response time of the stepping motor when the calculation time of a single search is greater than the rotation time of the stepping motor. Shorter and more efficient. Compared with the traditional closed-loop control method based on the hill-climbing algorithm, precise control can be achieved without receiving feedback information, and at the same time, the present invention can overcome the problem of local extreme points, and the control is simpler.

Description

technical field [0001] The invention belongs to a digital image processing technology, in particular to an iterative optimization automatic focusing method based on a hill-climbing method. Background technique [0002] As one of the key technologies of modern imaging equipment, automatic focusing technology is widely used in geological remote sensing, working condition detection, face recognition and other fields. As a common optical instrument, most microscopes are still dominated by manual focusing. In recent years, thanks to the development of digital image processing technology and the upsurge of industrial automation, the automatic focusing method based on image processing has become a research hotspot. The automatic focusing method based on image processing is an integrated technology that integrates optical, mechanical, electrical and other disciplines. The system has short response time, high focusing accuracy, and does not require the use of ranging sensors, so the...

AI Technical Summary

Problems solved by technology

[0003] The hill-climbing algorithm is the most common focus search strategy. Since the left side of the peak point of the ideal sharpness evaluation function is monotonically increasing and the right side is monotonically decreasing, the peak point corresponds to the focus position, and the sharpness evaluation function on both sides of the peak point is monotonous. The peak point can be searched by the change of , the disadvantage of the hill-climbing search method is that when the sharpness evaluation function has a local extreme point, there will be a problem that the local extreme point is searched and the focus fails.

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