基于自适应融合的手掌静脉增强方法

Palm vein enhancement method based on adaptive fusion

针对掌脉轮廓不清晰,图像对比度低、亮度低,进而导致识别性能降低的现象,提出一种自适应融合的手掌静脉增强方法。首先,基于暗原色先验(DCP)去雾算法,根据掌脉图像变异系数自适应选择去雾系数,得到DCP增强图像,并且基于部分子块重叠直方图均衡(POSHE)算法得到POSHE增强图像;然后,将图像分为16个子块,依据图像灰度均值与标准差确定各子块权重;最后,根据各子块权重对DCP和POSHE增强图像进行自适应融合,得到最终增强图像。该方法既保留了DCP算法在增强图像对比度和亮度的同时不引入明显噪声的优点,又保留了POSHE算法在增强图像对比度和亮度的同时不损失局部细节的特点;同时,两者的自适应融合既解决了DCP图像阴影部分掌脉缺失现象,又削弱了POSHE产生的块效应。在对两个公开库和自建库分别进行的实验中,三个数据库的等错误率分别为0.000 4、0.047 2、0.057 9,识别率分别为99.98%、94.27%、92.05%。实验结果表明,与现有的图像增强方法相比,该方法降低了等错误率,提高了识别精度。

To solve the degradation of recognition performance caused by unclear palm vein contour,low image contrast and brightness,a new palm vein enhancement method based on adaptive fusion was proposed.Firstly,based on Dark Channel Prior(DCP)defogging algorithm and adaptively selected defogging coefficient according to variation coefficient of the palm vein image,DCP enhanced image was obtained.And based on Partial Overlapped Sub-block Histogram Equalization(POSHE)algorithm,POSHE enhanced image was obtained.Secondly,the image was divided into 16 sub-blocks,and the weight of each sub-block was determined by the gray mean and the standard deviation.Finally,two kinds of enhanced images were fused adaptively according to the weight of each sub-block,obtaining the adaptive fused enhanced image.This method not only retains the advantages of DCP algorithm in enhancing image contrast and brightness without introducing significant noise,but also preserves the benefits of POSHE algorithm in enhancing image contrast and brightness without losing local details.Meanwhile,adaptive fusion of the two algorithms solves the problem of missing palm vein in shadow areas of DCP images and reduces the blocking artifacts produced by POSHE.Experimental results carried out on two public databases and a self-built database show that the equal error rates are 0.000 4,0.047 2,0.057 9 and the correct recognition rates are 99.98%,94.27%,92.05% respectively,indicating that compared with existing image enhancement methods,the proposed method reduces the equal error rate and improves the recognition accuracy.