确定图像是否模糊

问题描述

我看到很多关于此的话题，我理解了这个理论，但我无法对此进行编码。

我有一些图片，我想确定如果它们模糊或不模糊。我找到了一个图书馆（

我的代码在c＃中：

  public Bitmap PerformFFT（Bitmap Picture）
 {
 // Loade Image 
 ComplexImage output = ComplexImage.FromBitmap（Picture）; 
 
 //执行FFT 
 output.ForwardFourierTransform（）; 
 
 //返回图片
 return = output.ToBitmap（）; 
} 
  

如何判断图像是否模糊？我对这个理论不太满意，我需要具体的例子。我看到，但我不知道该怎么做。

编辑：

我会澄清我的问题。当我有一个复杂的 ComplexImage输出的二维数组（图像FFT）时，我可以使用什么C＃代码（或伪代码）来确定图像是否模糊？

解决方案

模糊图像具有FFT结果，在高频区域具有较小幅度。索引较低的数组元素（ Result [0] [0] 附近）表示低频区域。

所以按照一些标准划分得到的数组，两个区域的总和大小并进行比较。例如，选择四分之一的结果数组（大小为M），其中索引< M / 2 和 indexy< M / 2

对于一系列越来越模糊的图像（对于相同的初始图像），您应该看到越来越高的比率总和（低）/总和（高）

结果是方阵NxN。它具有中心对称性（ F（x，y）= F（-x，-y）因为源是纯实的），所以它足以处理数组的上半部分 y< N / 2 。

低频组件位于左上角和右上角附近数组的最小值（y的最小值，x的最小值和最大值）。所以数组元素的大小在范围内

  y的范围为0..N / 2 
，范围为x 0..N 
 amp = magnitude（y，x）
 if（y  low = low + amp 
 else 
 high = high + amp 
  

请注意，您的图片显示了混乱的数组 - 这是在中心显示零组件的标准做法。

I saw a lot a topic about this, I understood the theory but I'm not able to code this.

I have some pictures and I want to determine if they are blurred or not. I found a library (aforge.dll) and I used it to compte a FFT for an image.

As an example, there is two images i'm working on :

My code is in c# :

public Bitmap PerformFFT(Bitmap Picture)
{
    //Loade Image
    ComplexImage output = ComplexImage.FromBitmap(Picture);

    // Perform FFT
    output.ForwardFourierTransform();

    // return image
    return = output.ToBitmap();
}

How can I determine if the image is blurred ? I am not very comfortable with the theory, I need concret example. I saw this post, but I have no idea how to do that.

EDIT:

I'll clarify my question. When I have a 2D array of complex ComplexImage output (image FFT), what is the C# code (or pseudo code) I can use to determine if image is blurred ?

解决方案

Blurred image has FFT result with smaller magnitude in high-frequency regions. Array elements with low indexes (near Result[0][0]) represent low-frequency region.

So divide resulting array by some criteria, sum magnitudes in both regions and compare them. For example, select a quarter of result array (of size M) with index<M/2 and indexy<M/2

For series of more and more blurred image (for the same initial image) you should see higher and higher ratio Sum(Low)/Sum(High)

Result is square array NxN. It has central symmetry (F(x,y)=F(-x,-y) because source is pure real), so it is enough to treat top half of array with y<N/2.

Low-frequency components are located near top-left and top-right corners of array (smallest values of y, smallest and highest values of x). So sum magnitudes of array elements in ranges

for y in range 0..N/2
   for x in range 0..N
      amp = magnitude(y,x)
      if (y<N/4) and ((x<N/4)or (x>=3*N/4))
          low = low + amp
      else
          high = high + amp

Note that your picture shows jumbled array pieces - this is standard practice to show zero component in the center.

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