果蝇算法与改进蚁群算法优化模糊集的自适应图像增强

Self-adaption Image Enhancement Algorithm of Fruit Fly Optimization Algorithm and Improved Ant Colony Optimization

计算机视觉图像在采集和传输过程中,容易受到噪声的干扰而变得模糊不清,传统的模糊集图像增强算法具有计算量大、参数手动设置和适应性差的缺点,使得图像处理效率低下和增强质量较差,无法满足现实需求。本文将改进的蚁群算法引入计算机视觉图像模糊增强,以模糊熵为图像增强效果的评价指标,并对模糊图像增加参数进行自适应选择。结果表明,本算法可以提高图像的模糊熵、改善图像视觉效果和清晰度,同时可以较好地突出某些特征。

In the course of collecting and transmitting computer visual images, they are easy to become blurred by noise. Thetraditional fuzzy set image enhancement algorithm has the disadvantages of large computation, manual setting parametersand poor adaptability, which makes the image processing efficiency and the image quality poor so as to not meet the needs ofreality. The improved ant colony algorithm was introduced into computer vision image fuzzy enhancement whose effect wasevaluated by the fuzzy entropy and add parameter into fuzzy images to self-adaptive selection. The results showed that thealgorithm could improve the fuzzy entropy, the visual effect and sharpness and highlight some features on image.