Research On Low Illumination Image Enhancement Based On Metaheuristic Algorithm

In real life,due to the lack of light,low illuminance images are often produced.Such images first bring people visual discomfort,and at the same time,it is not conducive to the subsequent image processing.In order to improve the visual effect of such images,it is necessary to enhancing these images.The basic principle of the metaheuristic algorithm is to imitate various behaviors of animals in nature,and then form their behaviors into mathematical models.The metaheuristic algorithm can be used for function optimization to obtain the optimal solution.In this thesis,we regard the low illuminance image enhancement as a problem of transform function optimization,explore the enhancement method based on metaheuristic algorithm to enhancing the low illuminance images,and propose low illumination image enhancement methods based on particle swarm optimization algorithm and cuckoo search algorithm.The specific work and main contributions of this paper are as follows:(1)Aiming at solving the problems of overall darkness,uneven illumination and low contrast of image under low illumination conditions,we present a global and adaptive contrast enhancement algorithm for low illumination gray images in this paper.The algorithm is based on the Bilateral Gamma Adjustment function and combined with the particle swarm optimization(PSO).We use PSO to optimize the parameter(?)value of the Bilateral Gamma Adjustment function in order to prevent excessively enhances the brighter local areas in an image while enhancing details.(2)In order to effectively improve the visual effect and image quality of color images under low illumination conditions,we propose an image enhancement method based on HSV and CIEL*a*b color spaces.Firstly,the contrast is enhanced in the L*a*b* color space by CLAHE.In order to improve the brightness of the image,we combine the proposed ACPSO with gamma correction function to process the image in HSV color space.At the same time,the saturation of image is enhanced in the S channel of HSV color space by improved adaptive stretching function.(3)The mine images generally have problems such as poor contrast,uneven lighting and blurring.This paper proposes an image enhancement method based on cuckoo search.This method is based on the HSV color space and uses the Cuckoo Search(CS)algorithm combined with the proposed BGDPH algorithm.By Integrating the average brightness into the evaluation function,entropy,brightness difference,and standard deviation of grayscale are used as objective functions for each bird's nest,to evaluate the results of enhancing mine image.By virtue of performing global contrast and brightness enhancement on images by finding optimal parameter values,we achieve the detail enhancement of the mine image.(4)Experimental results show that using the method proposed in this paper to enhance low illuminance images with different characteristics,the obtained enhanced images have clear details and natural enhancement results.In addition,the proposed methods can effectively suppress the generation of noises and significantly improve the quality of low illumination images.