多尺度卷积结合自适应双区间均衡化的图像增强

Multi-scale Convolution Combined with Adaptive Bi-interval Equalization for Image Enhancement

为了解决红外图像对比度低、细节模糊的问题,提出了多尺度卷积结合双区间自适应亮度均衡化的红外图像增强方法.首先采用多尺度卷积对图像进行预处理;然后以最大化类内方差且最小化类间方差作为遗传算法适应度函数求解图像亮暗图层的划分阈值,并采用引入细节信息的双区间直方图进行均衡化,同时通过引入均方差和均值的灰度均匀化方式提高图像亮度;最后,将自适应受限拉普拉斯算子提取的细节图像与亮度提升的图像进行线性加权融合重构出细节边缘清晰、对比度较强的图像.采用不同场景下红外图像和细节丰富的灰度图像进行试验并与传统方法进行对比来验证该方法的有效性.本文方法处理后的图像信息熵(Entropy,En)、熵增强(Enhancement by Entropy,EME)和平均梯度(Average Gradient,AG)最大增幅分别由原来的5.0391、13.4461和7.8450增加到7.1633、90.2525和53.6177,表明该方法具有更好的性能.

In order to solve the problem of low contrast and fuzzy details of infrared image,an infrared image enhancement method based on multi-scale convolution combined with adaptive bi-interval luminance equalization is proposed.Firstly,the image is pre-processed by multi-scale convolution;Then,the threshold of image segmentation is solved by genetic algorithm,where the function of maximizing intra class variance and minimizing inter class variance is taken as its fitness function,the double interval histogram with detail information is used to equalize,and the brightness of image is improved by introducing the gray level homogenization of mean square and mean square.Finally,the image with clear details and strong contrast is reconstructed by linear weighted fusion of the detail image extracted by adaptive limited Laplace and the image with brightness enhancement.Compared with conventional methods in the infrared images of different scenes and gray images with abundant details to verify the validity of the proposed method,the maximum growth rates of En,EME and AG in images processed by this method increased from 5.0391,13.4461 and 7.8450 to 7.1633,90.2525 and 53.6177,respectively.The experimental results show that this method has better performance.