基于光流阈值的雾霾视频图像清晰度处理

Ru Chen, Xijuan Wang
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引用次数: 0

摘要

针对雾霾天气对视频图像视觉效果造成的图像失真、图像质量下降、视频图像清晰度模糊等问题,提出了一种基于光流阈值的雾霾视频图像去雾处理方法,以恢复真实自然的彩色图像。首先,提取图像的帧在时间t, t跟踪图像的特征在时间t + 1 t + n,提取图像的t + n帧,然后计算出的光流值t和t + n帧,区别对待获得的光流值获取光学流阈值,比较了光流阈值与给定的阈值,如果该值大于或等于给定的阈值,取光流阈值中间帧图像,采用Retinex算法对中间帧和t+n帧图像进行处理,该操作迭代进行。最后将处理后的单帧视频序列合并成一个整体输出。实验表明,该算法的处理速度为0.07,远低于其他处理方法,验证了所提算法的有效性和创新性。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Haze video image Clarity Processing Based on Optical Flow Threshold
In view of the problem of haze weather on the visual effect of video image, which causes the picture distortion, image quality degradation and definition blur of video image, a defogging processing method of haze video image based on optical flow threshold is proposed so as to restore the real and natural color image. Firstly, extract the image of the t frame at time t, track the characteristics of the image at time t + 1 to time t + n, extract the image of the t+n frame, then calculate the optical flow values of the t frame and the t + n frame, make a difference between the obtained optical flow values to obtain the optical flow threshold, compare the obtained optical flow threshold with the given threshold, if the value is greater than or equal to the given threshold, take the optical flow threshold intermediate frame image, and the middle frame and t+n frame images are processed by Retinex algorithm, and this operation is performed iteratively. Finally, the processed single frame video sequence is merged into a whole and output. The experiment shows that the processing speed of the algorithm is 0.07, much lower than other processing methods, which verifies the effectiveness and innovativeness of the proposed algorithm.
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