结合风驱动优化的低复杂度图像增强方法

LOW COMPLEXITY IMAGE ENHANCEMENT METHODCOMBINED WITH WIND DRIVEN OPTIMIZATION

针对复杂环境下如光照较弱、雾天等条件下拍摄的图像存在对比度不足、整体偏暗等问题,提出一种结合智能风驱动优化的低复杂度的图像增强方法。该方法利用双曲正弦函数、伽马校正函数、Sigmoid函数、对比度拉伸函数对图像进行校正。针对图像增强过程中伽马校正参数与对比度拉伸函数中动态因子的参数选择问题,利用智能风驱动算法,将图像信息熵与标准差构造适应度函数进行参数寻优,获取最优参数。将该方法与直方图均衡化法、多尺度Retinex算法、基于引导滤波的Retinex算法比较。实验结果表明该算法简单,图像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。

In the complex environment,such as light illumination,fog and other conditions,the image has a lack of contrast,and the whole is dark.In view of this problem,a low complexity image enhancement method combined with intelligent wind driven optimization is proposed.This method used hyperbolic sine function,Gamma correction function,Sigmoid function and contrast stretching function to correct the image.A contrast stretching function was used to stretch the contrast of the image.For the parameter selection of dynamic factors in Gamma correction parameters and contrast stretching functions during image enhancement process,the intelligent wind driven algorithm which used the image entropy and the ratio to construct the fitness function was used to obtained the optimal parameters.The method was compared with histogram equalization,multi-scale Retinex algorithm and Retinex algorithm based on guided filtering.The experimental results show that this algorithm is simple and its image enhancement effect is better than the other algorithms,and the quality and contrast of the image are improved.