Image enhancement method based on adaptive immunity genetic algorithm

Abstract

The invention discloses an image enhancement method based on an adaptive immunity genetic algorithm. The image enhancement method based on the adaptive immunity genetic algorithm includes steps that S1, normalizing an image pixel gray level f(x, y) to obtain n(x, y); S2, coding parameters (alpha, beta) to be optimized, randomly generating a group of initial individuals to form an initial population, and inputting a control parameter crossover probability p<c>, a mutation probability p<m>, a population size N, a maximum running algebra G and the like; S3, judging whether an evolution algebra t is equal to G, if so, ending the algorithm, and outputting the optimal solution of (alpha, beta), otherwise, turning to the next step; S4, using a roulette strategy to select M individuals, and carrying out crossover and mutation operations on the individuals according to crossover and mutation methods in genetic operation; S5, selecting two vaccines, the individuals to be vaccinated and a vaccination point number to perform immunization, making a immunization choice after the vaccination, and using the optimal individual retention strategy for the vaccinated population; S6, obtaining the corresponding nonlinear transformation function F(u) of each group of (alpha, beta), and using the nonlinear transformation function to perform an image gray level transformation to obtain an output image g(x, y).

Description

technical field [0001] The invention relates to the technical field of image enhancement, in particular to an image enhancement method based on adaptive immune genetic algorithm. Background technique [0002] The purpose of image enhancement is to: ① use a series of technologies to improve the visual effect of the image and improve the clarity of the image; ② convert the image into a form that is more suitable for human or machine analysis and processing. Image enhancement includes gray scale and contrast processing, noise removal, edge protrusion and sharpening, filtering, interpolation and amplification, and false color processing, etc. Currently commonly used enhancement techniques can be divided into spatial domain image enhancement method and frequency domain image enhancement method. The former directly processes the image pixels, while the latter performs Fourier transform on the image before processing. Spatial domain enhancement methods include grayscale transform...

AI Technical Summary

Problems solved by technology

Based on the principle of artificial immunity, some researchers have adopted a new fitness function for image quality evaluation, including five elements of variance, information entropy, compactness, signal-to-noise change, and pixel difference. Some effects have been achieved, but what they...

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