基于求爱混合麻雀搜索算法的图像增强

Image enhancement based on courtship hybrid sparrow search algorithm

为了解决低照度图像中存在边缘模糊、对比度差和清晰度低等问题,提出了一种基于改进麻雀搜索算法的图像增强算法。首先利用立方体映射(cubic mapping, CM)初始化种群,增加麻雀种群的多样性;然后使用求爱学习策略优化追随者麻雀的位置更新,增强算法的收敛精度和速度;同时为了避免算法陷入局部极值,引入黄金正弦策略来更新发现者位置,使得全局搜索与局部开发能力进一步的得到平衡;最后引入双伽马函数,通过改进的麻雀搜索算法寻找最优伽马值,实现图像的全局增强。通过在6个标准测试函数上与其他算法进行对比,实验数据表明,与当前其他图像增强算法比较,该方法增强效果更好,得到图像质量更佳。

In order to solve the problems of edge blur, poor contrast and low definition in low illumination images, an image enhancement algorithm based on improved sparrow search algorithm is proposed. Firstly, cubic mapping is used to initialize the population and increase the diversity of sparrow population. Then the courtship learning strategy is used to optimize the location update of the follower sparrow to enhance the convergence accuracy and speed of the algorithm. At the same time, in order to avoid the algorithm falling into local extremum, the golden sine strategy is introduced to update the location of the discoverer, so that the global search and local development ability can be further balanced. Finally, the double Gamma function is introduced to find the optimal Gamma value through the improved sparrow search algorithm to realize the global enhancement of the image. By comparing with other algorithms on six standard test functions, the experimental data show that compared with other current image enhancement algorithms, this method has better enhancement effect and better image quality.