Infrared image enhancement method based on particle swarm optimization

Abstract

The invention provides an infrared image enhancement method based on particle swarm optimization, global gamma correction is carried out by adopting a particle swarm optimization algorithm in combination with an image gamma correction method, and the method comprises the following steps: firstly, carrying out graying on an infrared image and initializing a population; Next, for each particle, carrying out Gamma correction on the grayed image to obtain an intermediate enhanced image.; calculating the entropy value of the intermediate imagecalculated; calculating fitness values of the particlesthrough weighted fusion of edge contents and gray scale standard variances, iteratively updating individual and group optimal fitness values, iteratively updating particle speeds and positions by timely adjusting learning factors, and continuously iteratively optimizing to find out final group optimal positions as optimal gamma values to enhance images. When the low-contrast infrared image is enhanced, the entropy value of the image is maximized, the edge is clear, the structure information of the original image is reserved, and the enhancement result is more natural; When the highlight area is enhanced, noise and artifacts are not easily generated, and the infrared image can be obviously improved.

Description

technical field [0001] The invention belongs to the technical field of image processing, and in particular relates to an infrared image enhancement method based on particle swarm optimization. Background technique [0002] With the continuous development of science and technology, infrared imaging, as the product of the combination of infrared technology and imaging technology, is more and more widely used. It has been applied to many fields such as security monitoring, military target detection and tracking, and medical treatment. Infrared radiation converts temperature information into grayscale information. When actually detecting the temperature of an object, it is easily affected by heat transfer, thermal radiation and atmospheric attenuation, resulting in low image contrast and unclear texture details, etc., where the contrast between the target and the background is low, which makes it difficult to identify the original infrared image. Background and target objects b...

AI Technical Summary

Problems solved by technology

[0004] Aiming at the technical problem that the existing method does not consider the characteristics of low contrast and unclear texture details of the infrared image itself, resulting in poor infrared image enhancement effect, the present invention proposes an infrared image enhancement method based on particle swarm optimization. The technology is combined with the classic contrast enhancement technology, the particle swarm optimization algorithm is combined with the image gamma correction method to perform global gamma correction, and the gray standard variance, entropy value and edge content are fused to construct an evaluation function, and timely adjustment learning is adopted The factor method updates the particle velocity and position, avoids the premature convergence of the algorithm, prevents the particles from falling into local optimum, and finds the optimal gamma value to enhance the infrared image, which can achieve a more natural and obvious enhancement effect on low-contrast infrared images. Improve infrared images with highlighted areas

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