A Focusing Method Based on Quantum Particle Swarm Optimization Algorithm

Abstract

A focusing method based on quantum particle swarm optimization algorithm, which is used to focus on images when taking images, is characterized in that it comprises the following steps: Step 1, using a plurality of particles in three-dimensional space to represent the pixel points in the image respectively Gray value, the gray value represented by the particle is randomly set; step 2, the initial position of the particle in the quantum particle swarm is evenly distributed according to the position formula; step 3, the average gray value of the foreground image and the background image of the image is used The value variance function calculates the fitness value of the particle, and uses the quantum particle swarm optimization algorithm combined with the domain search method to obtain the optimal segmentation threshold; step 4, divides the image into foreground and background images according to the optimal segmentation threshold; step 5, according to The center of gravity of the gray value of the foreground image selects the focus area; step 6, use the gray difference method as the image sharpness evaluation function, and determine the position of the lens according to the function value calculated by the image sharpness evaluation function to complete the focusing.

Description

technical field [0001] The invention relates to the technical field of image focusing, in particular to a focusing method based on a quantum particle swarm optimization algorithm. Background technique [0002] Focusing mainly includes three basic parts, namely focusing area selection, image definition evaluation, and search algorithm. Traditional image focusing techniques include ranging method, focus detection method, and phase focusing method. Commonly used focusing area selection algorithms include center windowing. , multi-point windowing, non-uniform sampling windowing, pupil tracking method and skin detection method, the central windowing does not have the ability to adapt to the imaging position of the main target; multi-point windowing introduces more background, and the calculation amount is larger , the focusing speed slows down; non-uniform sampling windowing also does not have the ability to adapt, and a large number of floating-point calculations affect the focu...

AI Technical Summary

Problems solved by technology

[0002] Focusing mainly includes three basic parts, namely focusing area selection, image definition evaluation, and search algorithm. Traditional image focusing techniques include ranging method, focus detection method, and phase focusing method. Commonly used focusing area selection algorithms include center windowing. , multi-point windowing, non-uniform sampling windowing, pupil tracking method and skin detection method, center windowing does not have the ability to adapt to the imaging position of the main target; multi-point windowing introduces more background, and the calculation amount is larger , the focusing speed slows down; non-uniform sampling windowing also does not have adaptive capabilities, and a large number of floating-point operations affect the focusing speed; the pupil tracking rule requires that the pupil information of the photographer must be obtained, and the scope of use of skin detection is even smaller.

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