Improved image enhancement method

Abstract

The present invention provides an improved image enhancement method. The method comprises: S1, performing normalization processing of image pixel gray level f(x, y) to obtain n(x, y), wherein an improved normalization processing mode is employed: (img file='dest_path_image001. TIF' wi='255' he='183' / ); S2, performing coding of parameters to be optimized (img file='dest_path_image002. TIF' wi='37' he='71' / ), randomly generating one group of initial individuals to form an initial population, and inputting a control parameter crossover probability pc, a mutation probability pm, a population size N, the maximum operation algebra G and the like; S3, determining whether an evolution algebra t is equal to the G or not, if the evolution algebra t is equal to the G, finishing the algorithm, and outputting the optimal solution of the (img file='dest_path_image003. TIF' wi='37' he='88' / ), or else, turning to the next step; S4, employing a roulette strategy to select M individuals, and performing crossover and mutation operation of the individuals according to the crossover and mutation method in the genetic manipulation; S5, selecting two vaccines, individual number to be inoculated and the inoculation site number to perform immunization operation, making out immunization selection after inoculation, and employing the optimal individual reservation strategy of the population after the inoculation; and S6, allowing one group (img file='dest_path_image004. TIF' wi='37' he='71' / ) to correspond to one non-linear transformation function F(u), and employing a non-linear transformation function to perform image gray scale 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 improved image enhancement method. 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 transformation, histogram transformation, i...

AI Technical Summary

Problems solved by technology

Based on the principle of artificial immunity, some researchers 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 have in common is that the improvements are all one-sided, that is to say, various improvements are made from different angles, so the effects are not the same

Moreover, most of them have shortcomings such as narrow applicability, the need to pre-set the threshold, and a large amount of calculation.

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