Global self-adaptive grayscale image enhancement method based on double gamma correction

Abstract

The invention discloses a global self-adaptive gray level image enhancement method based on double gamma correction, which adopts a particle swarm optimization algorithm combined with an image doublegamma function to carry out global double gamma correction, and mainly comprises the following steps of: inputting a gray level image and initializing parameters of the particle swarm optimization algorithm; performing double-gamma correction on the input image by adopting each particle position to obtain a preliminary enhanced image, calculating a corresponding fitness value, and updating a historical optimal fitness value and an optimal position of the particle individual and the group; judging whether an iterative optimization termination condition is met or not, and if not, updating the inertia weight, the learning factor, the speed and the position of each particle and continuing iteration; otherwise, performing double-gamma correction on the input image by using the final group optimal position to obtain a final enhanced image. When the low-illumination grayscale image is enhanced, the contrast of the image can be effectively improved, excessive enhancement of a local bright areais avoided, the texture and detail information of the enhanced grayscale image are clear and complete, and the overall visual effect is improved.

Description

Technical field [0001] The present invention relates to the technical field of gray-scale image processing, in particular to an image enhancement method under low-illuminance uneven illumination conditions. Background technique [0002] In real life, due to insufficient lighting conditions, low-illuminance grayscale images with non-uniform illumination are often produced. Such images first bring people visual discomfort, because these images have unobvious differences in the details of the analyzed objects and poor illumination. It is difficult to identify the uniform target and other problems, and it is also not conducive to the subsequent image processing work. In order to improve the visual effect of such images, it is necessary to enhance them. [0003] Image enhancement can be divided into global enhancement and local enhancement. Global enhancements such as histogram equalization (HE) algorithms usually over-enhance brighter local areas, causing problems such as diffusion of...

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Problems solved by technology

[0004] Aiming at gray-scale images with uneven illumination and low overall contrast, in order to solve the problems of inconspicuous detail enhancement and excessive enhancement of local bright areas in existing methods when image enhancement occurs, the present invention proposes a dual-gamma correction-based global Adaptive grayscale image enhancement method, which combines swarm intelligence technology with contrast enhancement technology, uses particle swarm optimization algorithm combined with double gamma correction method for global correction, integrates grayscale standard variance into evaluation function, and combines entropy and edge content to construct The evaluation function uses the method of timely adjusting the learning factor to update the particle speed and position, avoids premature convergence of the al

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