通过图像训练改善自动驾驶汽车的视觉

Lokesh babu Gangula, G. Srikanth, Ch. Naveen, V. Satpute
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引用次数: 1

摘要

在雨天开车会导致许多事故。这是自动驾驶汽车的主要问题,因此它们没有得到很多推广。因此,为了加强安全措施,我们实施了这项工作,即在下雨的情况下从捕获的图像中删除雨水成分。根据雨的特征从图像中去除雨的成分。彩色图像分为高频部分和低频部分,其中高频部分包含了大部分雨分量。然后利用字典学习方法提取高频部分的雨分量。为了提取更多的非雨细节,我们使用了颜色通道方差敏感性(SVCC)。最后,将无雨分量与低频分量相结合,得到无雨图像。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Vision Improvement in Automated Cars by Image Deraining
Driving cars in rainy situations lead to many accidents. This is the major issue with the automated cars and hence they are not promoted a lot. So in order to enhance the safety measure, we implemented this work of removing the rain components from a captured image during rainy situations. The rain components are removed from that image based on the rain characteristics. The colored image is divided into high frequency and low-frequency parts so that the high-frequency part consists of most of the rain components. Then by using Dictionary learning method the rain components are extracted from the high-frequency part. To extract more non-rain details we use Sensitivity of variance of color channels(SVCC).Finally, the non-rain component part and low-frequency part are combined to get the image without rain.
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