基于非下采样contourlet系数尺度评价的路面图像增强

Li He, Shiru Qu, Daqi Zhang
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引用次数: 0

摘要

本文介绍了一种基于非下采样contourlet变换的尺度评价方法及其在路面图像裂缝检测中的应用。本文提出了一种尺度评价方法,并在尺度评价后对不同尺度进行不同增益的增强。第一步,通过噪声估计计算噪声阈值;然后,在每个尺度下从完整图像中划分出像素为64×64的子组,并计算这些子组的组方向方差进行尺度评估。最后,结合尺度评价的结果,对不同尺度下的增强过程进行了分析。实验结果表明,该方法在路面图像增强中具有良好的应用前景。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Pavement image enhancement based on scale evaluation using nonsubsampled contourlet coefficients
This paper describes a scale evaluation method using nonsubsampled contourlet transform and its application in pavement image enhancement for crack detection. Crack in some scales is much more visible than in others, so a method for scale evaluation is given, and different gains are delivered to each scale for enhancement after scale evaluation. In the first step, noise threshold is computed by noise estimation. And then, sub-groups with 64×64 pixels are divided from the full image at each scale, and group direction variances of these sub-groups are computed for scale evaluation. At last, enhancing process at different scales are taken with gains obtained from scale evaluation. Experiment results show a promising use of the presented method for pavement image enhancement.
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