张方, 肖辉. 基于三角函数变换与IRDPSO优化的图像增强算法[J]. 红外与激光工程, 2022, 51(8): 20210709. DOI: 10.3788/IRLA20210709
引用本文: 张方, 肖辉. 基于三角函数变换与IRDPSO优化的图像增强算法[J]. 红外与激光工程, 2022, 51(8): 20210709. DOI: 10.3788/IRLA20210709
Zhang Fang, Xiao Hui. Image enhancement algorithm based on trigonometric function transformation and IRDPSO optimization[J]. Infrared and Laser Engineering, 2022, 51(8): 20210709. DOI: 10.3788/IRLA20210709
Citation: Zhang Fang, Xiao Hui. Image enhancement algorithm based on trigonometric function transformation and IRDPSO optimization[J]. Infrared and Laser Engineering, 2022, 51(8): 20210709. DOI: 10.3788/IRLA20210709

基于三角函数变换与IRDPSO优化的图像增强算法

Image enhancement algorithm based on trigonometric function transformation and IRDPSO optimization

  • 摘要: 针对复杂环境下如阴天、雾天、夜晚、光照较弱等条件下拍摄的图像存在对比度不足、整体偏暗等问题,提出了一种基于三角函数变换与改进随机漂移粒子群算法的图像增强算法。该图像增强方法主要包括四个步骤,首先将彩色图像转换为灰度图像,然后对灰度图像利用三角函数变换提高对比度,然后再对图像进行拉布拉斯算子增强,最后再对图像进行色彩恢复。为了提高算法的自适应性,针对三角函数变换中的参数、以及拉布拉斯算子模板的参数选择问题,将改进随机漂移粒子群算法(IRDPSO)与图像增强算法结合,利用信息熵和图像标准差构造适应度函数,对参数进行寻优。将该方法与其他四种算法进行比较。实验结果表明:文中算法简单,增强后的图像信息熵值、标准差值均较大,图像颜色失真度小,增强效果均比其他几种算法好,提高了图像的质量和对比度。

     

    Abstract: In the complex environment, such as cloudy days, foggy days, night, weaker light illumination and other conditions, the image has a lack of contrast, and the whole is dark. In view of this problem, an image enhancement algorithm based on trigonometric function transformation and IRDPSO is proposed. The image enhancement method mainly consists of four steps. First, the color image is converted to a gray image. Then, the contrast of the grayscale image is improved by trigonometric function transformation. Then, the image is enhanced by the Laplacian operator. Finally, a color restoration process is applied to the image. Aiming at the parameters in trigonometric function transformation and the parameter selection problem of the Laplacian operator, the improved random drift particle swarm optimization (IRDPSO) algorithm is combined with an image enhancement algorithm, the fitness function is constructed by information entropy and image standard deviation, and the parameters are optimized. The proposed algorithm is compared with four other algorithms. The experimental results show that the proposed algorithm is simple, the image information entropy is enhanced, the standard difference is large, the color distortion of the image is small and the enhancement effect is better than that of the other algorithms, and the quality and contrast of the image are improved.

     

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