Image enhancement methods are typically aimed at improvement of the overall visibility of features. Though histogram equalization can enhance the contrast by redistributing the gray levels, it has the drawback that it...Image enhancement methods are typically aimed at improvement of the overall visibility of features. Though histogram equalization can enhance the contrast by redistributing the gray levels, it has the drawback that it reduces the information in the processed image. In this paper, we present a new image enhancement algorithm. After histogram equalization is carried out, morphological filters and wavelet-based enhancement algorithm is used to clean out the unwanted details and further enhance the image and compensate for the information loss during histogram equalization. Experimental results show that the morphological filters and wavelet-based histogram equalization algorithm can significantly enhance the contrast and increase the information entropy of the image.展开更多
针对ORB(oriented FAST and rotated BRIEF)算法中存在匹配精确率低的问题,提出了一种基于LK(Lucas-Kanade)光流改进的ORB图像匹配方法。首先对待处理的图像进行直方图均衡化,然后在Oriented FAST特征点检测的同时用LK光流对其进行跟踪...针对ORB(oriented FAST and rotated BRIEF)算法中存在匹配精确率低的问题,提出了一种基于LK(Lucas-Kanade)光流改进的ORB图像匹配方法。首先对待处理的图像进行直方图均衡化,然后在Oriented FAST特征点检测的同时用LK光流对其进行跟踪,并将跟踪的特征点进行Rotated BRIEF描述,最后在特征匹配筛选环节利用RANSAC(Random Sampling Consistency)算法进行误匹配的剔除。实验结果表明,改进算法在公开数据集中的平均匹配精度为90.9%,平均特征匹配及误匹配的剔除共耗时为18ms,与原始ORB算法相比,在时间基本一致的前提下,有效的提高了匹配的精度。展开更多
文摘Image enhancement methods are typically aimed at improvement of the overall visibility of features. Though histogram equalization can enhance the contrast by redistributing the gray levels, it has the drawback that it reduces the information in the processed image. In this paper, we present a new image enhancement algorithm. After histogram equalization is carried out, morphological filters and wavelet-based enhancement algorithm is used to clean out the unwanted details and further enhance the image and compensate for the information loss during histogram equalization. Experimental results show that the morphological filters and wavelet-based histogram equalization algorithm can significantly enhance the contrast and increase the information entropy of the image.
文摘针对ORB(oriented FAST and rotated BRIEF)算法中存在匹配精确率低的问题,提出了一种基于LK(Lucas-Kanade)光流改进的ORB图像匹配方法。首先对待处理的图像进行直方图均衡化,然后在Oriented FAST特征点检测的同时用LK光流对其进行跟踪,并将跟踪的特征点进行Rotated BRIEF描述,最后在特征匹配筛选环节利用RANSAC(Random Sampling Consistency)算法进行误匹配的剔除。实验结果表明,改进算法在公开数据集中的平均匹配精度为90.9%,平均特征匹配及误匹配的剔除共耗时为18ms,与原始ORB算法相比,在时间基本一致的前提下,有效的提高了匹配的精度。
