计算机视觉：简单的降噪
在计算机视觉中，我们经常想要去除图像中的噪声。我们可以通过获取图像并用其邻居的平均值替换扭曲像素来实现此目的。我很容易理解这一点，但是以下方程中的所有变量意味着什么？我刚刚在一些幻灯片中找到了它，但它没有任何解释：
(i,j ）可能是给定像素及其邻居，但函数 f、Omega 和 w 是什么？有什么猜测吗？
干杯，伙计们。
In computer vision, we often want to remove noise from an image. We can do this by getting an image and replacing distorted pixels with an average of its neighbours. I have no trouble understanding this but what are all the variables in the following equation meant to be? I've just found it in some slides but it doesn't come with any explanation:
The (i,j) is probably a given pixel and its neighbour, but what is the function f, the Omega, and the w? Any guesses?!
Cheers guys.
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这实在是太模糊了。论文和不同方法之间的符号会发生变化。一般来说，该公式在 i,j 点（由 \Omega_{ij} 中的点定义）的相邻集合内进行一些平均
w是一些归一化常数，f(m, n)是一些函数，通常为 m,n 分配一个值，该值与它到 i,j 的距离成正比，正如我所说，你的问题有点太模糊了，无法说其他任何东西......
This is way too vague. Notation changes between papers and different approaches. Generally speaking that formula is doing some averaging within a neighbouring set of the i,j point (defined by the points in \Omega_{ij})
wis some normalization constant andf(m,n)is some function which typically assigns a value to m,n proportional to its distance from i,jAs I said your question is a bit too vague to say anything else...
这看起来类似于视频编码中的运动预测。
g(i,j) 可能是块/屏幕中的第 i、j 个像素。其值是采用相邻位置 (m,n) 的另一个启发式函数的加权和，
因为我看到 Omega，所以我怀疑您正在信号空间中工作。这可能会过滤掉我们邻居 m,n 中未发现的高频
This looks similar to motion prediction in video encoding.
g(i,j) is likely the ith, jth pixel in a block / screen. whose value is the weighted sum of another heuristic function taking the neighbor positions (m,n)
Since I see Omega I suspect you are working in signal space. This might filter out high frequencies not found in our neighbors m,n