实时应用程序的OpenCV fastNlMeansDenoising的替代方法? [英] Alternative to OpenCV fastNlMeansDenoising for real time application?

问题描述

我目前正在使用下面的功能从两个尺寸为(240x720)的图像中消除噪点.我目前正在计算机上对此进行计算，但希望实时实现.但是，在我的计算机上，该功能会导致程序严重延迟.除了从图像中实时消除杂讯之外，还有其他选择吗?也许是高斯模糊?

I'm currently using the below function to remove noise from two images of size (240x720). I'm currently computing this on my computer but would like to implement this in real time. However on my computer the function results in significant delays to the program. Is there an alternative to removing noise from an image that could work in real time? Gaussian blur perhaps?

  fastNlMeansDenoising(ipmOfLeftLaneRobust, ipmOfLeftLaneRobust,10,7,21);

推荐答案

给出一个函数

fastNlMeansDenoising(InputArray src, OutputArray dst, float h=3, 
    int templateWindowSize=7, int searchWindowSize=21 );

当 templateWindowSize 接近1时，该过滤器将变为盒式双边过滤器.

When the templateWindowSize approaches 1, the filter becomes a box bilateral filter.

当 h 项接近无限时，该滤镜将成为盒模糊滤镜.

When the h term approaches infinite, the filter becomes a box blur filter.

对于任何两个，实现都是详尽无遗的.

For either two, the implementation is exhaustive.

一种直接的替代方法是先尝试两种优化的特殊情况: cv :: boxFilter 和 cv :: bilateralBlur .

A straightforward alternative is to try the two optimized special cases first: cv::boxFilter and cv::bilateralBlur.

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