将高斯模糊应用于频域图像

Question

我对在频域中对图像应用高斯模糊感到困惑。出于未知原因（可能我没有错）我收到有线图像而不是模糊图像。

这是我一步一步做的：

加载图像。

将图像拆分为单独的通道。

private static Bitmap[] separateColorChannels(Bitmap source, int channelCount)
{
    if (channelCount != 3 && channelCount != 4)
    {
        throw new NotSupportedException("Bitmap[] FFTServices.separateColorChannels(Bitmap, int): Only 3 and 4 channels are supported.");
    }

    Bitmap[] result = new Bitmap[channelCount];
    LockBitmap[] locks = new LockBitmap[channelCount];
    LockBitmap sourceLock = new LockBitmap(source);
    sourceLock.LockBits();

    for (int i = 0; i < channelCount; ++i)
    {
        result[i] = new Bitmap(source.Width, source.Height, PixelFormat.Format8bppIndexed);
        locks[i] = new LockBitmap(result[i]);
        locks[i].LockBits();
    }

    for (int x = 0; x < source.Width; x++)
    {
        for (int y = 0; y < source.Height; y++)
        {
            switch (channelCount)
            {
                case 3:
                    locks[0].SetPixel(x, y, Color.FromArgb(sourceLock.GetPixel(x, y).R));
                    locks[1].SetPixel(x, y, Color.FromArgb(sourceLock.GetPixel(x, y).G));
                    locks[2].SetPixel(x, y, Color.FromArgb(sourceLock.GetPixel(x, y).B));

                    break;
                case 4:
                    locks[0].SetPixel(x, y, Color.FromArgb(sourceLock.GetPixel(x, y).A));
                    locks[1].SetPixel(x, y, Color.FromArgb(sourceLock.GetPixel(x, y).R));
                    locks[2].SetPixel(x, y, Color.FromArgb(sourceLock.GetPixel(x, y).G));
                    locks[3].SetPixel(x, y, Color.FromArgb(sourceLock.GetPixel(x, y).B));

                    break;
                default:
                    break;
            }
        }
    }

    for (int i = 0; i < channelCount; ++i)
    {
        locks[i].UnlockBits();
    }

    sourceLock.UnlockBits();
}

将每个通道转换为复杂图像（AForge.NET）。

public static AForge.Imaging.ComplexImage[] convertColorChannelsToComplex(Emgu.CV.Image<Emgu.CV.Structure.Gray, Byte>[] channels)
{
    AForge.Imaging.ComplexImage[] result = new AForge.Imaging.ComplexImage[channels.Length];

    for (int i = 0; i < channels.Length; ++i)
    {
        result[i] = AForge.Imaging.ComplexImage.FromBitmap(channels[i].Bitmap);
    }

    return result;
}

应用高斯模糊。

首先我创建内核（出于测试目的，内核大小等于图像大小，只有它的中心部分是用高斯函数计算的，其余内核等于 re=1 im= 0).

private ComplexImage makeGaussKernel(int side, double min, double max, double step, double std)
{
    // get value at top left corner
    double _0x0 = gauss2d(min, min, std);

    // top left corner should be 1, so making scaler for rest of the values
    double scaler = 1 / _0x0;

    int pow2 = SizeServices.getNextNearestPowerOf2(side);

    Bitmap bitmap = new Bitmap(pow2, pow2, PixelFormat.Format8bppIndexed);

    var result = AForge.Imaging.ComplexImage.FromBitmap(bitmap);

    // For test purposes my kernel is size of image, so first, filling with 1 only.
    for (int i = 0; i < result.Data.GetLength(0); ++i)
    {
        for (int j = 0; j < result.Data.GetLength(0); ++j)
        {
            result.Data[i, j].Re = 1;
            result.Data[i, j].Im = 0;
        }
    }

    // The real kernel's size.
    int count = (int)((Math.Abs(max) + Math.Abs(min)) / step);

    double h = min;
    // Calculating kernel's values and storing them somewhere in the center of kernel.
    for (int i = result.Data.GetLength(0) / 2 - count / 2; i < result.Data.GetLength(0) / 2 + count / 2; ++i)
    {
        double w = min;
        for (int j = result.Data.GetLength(1) / 2 - count / 2; j < result.Data.GetLength(1) / 2 + count / 2; ++j)
        {
            result.Data[i, j].Re = (scaler * gauss2d(w, h, std)) * 255;
            w += step;
        }
        h += step;
    }

    return result;
}

// The gauss function
private double gauss2d(double x, double y, double std)
{
    return ((1.0 / (2 * Math.PI * std * std)) * Math.Exp(-((x * x + y * y) / (2 * std * std))));
}

对每个通道和内核应用 FFT。

将每个通道的中心部分乘以内核。

void applyFilter(/*shortened*/)
{
    // Image's size is 512x512 that's why 512 is hardcoded here
    // min = -2.0; max = 2.0; step = 0.33; std = 11
    ComplexImage filter = makeGaussKernel(512, min, max, step, std);

    // Applies FFT (with AForge.NET) to every channel and filter
    applyFFT(complexImage);
    applyFFT(filter);

    for (int i = 0; i < 3; ++i)
    {
        applyGauss(complexImage[i], filter, side);
    }

    // Applies IFFT to every channel
    applyIFFT(complexImage);
}

private void applyGauss(ComplexImage complexImage, ComplexImage filter, int side)
{
    int width = complexImage.Data.GetLength(1);
    int height = complexImage.Data.GetLength(0);

    for(int i = 0; i < height; ++i)
    {
        for(int j = 0; j < width; ++j)
        {
            complexImage.Data[i, j] = AForge.Math.Complex.Multiply(complexImage.Data[i, j], filter.Data[i, j]);
        }
    }
}

对每个通道应用 IFFT。

将每个通道转换回位图（AForge.NET）。

public static System.Drawing.Bitmap[] convertComplexColorChannelsToBitmap(AForge.Imaging.ComplexImage[] channels)
{
    System.Drawing.Bitmap[] result = new System.Drawing.Bitmap[channels.Length];

    for (int i = 0; i < channels.Length; ++i)
    {
        result[i] = channels[i].ToBitmap();
    }

    return result;
}

将位图合并为单个位图

public static Bitmap mergeColorChannels(Bitmap[] channels)
{
    Bitmap result = null;

    switch (channels.Length)
    {
        case 1:
            return channels[0];
        case 3:
            result = new Bitmap(channels[0].Width, channels[0].Height, PixelFormat.Format24bppRgb);
            break;
        case 4:
            result = new Bitmap(channels[0].Width, channels[0].Height, PixelFormat.Format32bppArgb);
            break;
        default:
            throw new NotSupportedException("Bitmap FFTServices.mergeColorChannels(Bitmap[]): Only 1, 3 and 4 channels are supported.");
    }

    LockBitmap resultLock = new LockBitmap(result);
    resultLock.LockBits();

    LockBitmap red = new LockBitmap(channels[0]);
    LockBitmap green = new LockBitmap(channels[1]);
    LockBitmap blue = new LockBitmap(channels[2]);

    red.LockBits();
    green.LockBits();
    blue.LockBits();

    for (int y = 0; y < result.Height; y++)
    {
        for (int x = 0; x < result.Width; x++)
        {
            resultLock.SetPixel(x, y, Color.FromArgb((int)red.GetPixel(x, y).R, (int)green.GetPixel(x, y).G, (int)blue.GetPixel(x, y).B));
        }
    }

    red.UnlockBits();
    green.UnlockBits();
    blue.UnlockBits();

    resultLock.UnlockBits();

    return result;
}

因此，我得到了移位的红色模糊图像版本：link。

@edit - 通过对代码进行几处更改来更新问题。

Answer 1

我在 DSP stackexchange 的一些帮助下弄明白了...和一些作弊但它有效。主要问题是内核生成和对其应用 FFT。同样重要的是，AForge.NET 在转换为 ComplexImage 期间将图像像素除以 255，并在从 ComplexImage 转换为位图期间乘以 255（感谢 Olli Niemitalo @ DSP SE）。

我是如何解决这个问题的：

我发现内核在 FFT 后应该是什么样子（见下文）。
查找了该图像的颜色。
计算出 x = -2 的 gauss2d； y = -2;标准 = 1.
计算预分频器以从 pt 中计算的值接收颜色值。 3（参见 wolfram）。
使用 pt 的 perscaler 生成具有缩放值的内核。 4.

但是我不能在生成的过滤器上使用 FFT，因为生成的过滤器看起来已经像 FFT 之后的过滤器了。它有效 - 输出图像模糊，没有伪影，所以我认为这还不错。

图片（我不能 post 超过 2 个链接，而且图片非常大）：

Input image
Generated filter (without FFT!)
- 以下函数的参数：
- 标准 = 1.0
- 大小 = 8.0
- 宽=高=512
Result image

最终代码：

private ComplexImage makeGaussKernel(double size, double std, int imgWidth, int imgHeight)
{
    double scale = 2000.0;
    double hsize = size / 2.0;

    Bitmap bmp = new Bitmap(imgWidth, imgHeight, PixelFormat.Format8bppIndexed);
    LockBitmap lbmp = new LockBitmap(bmp);

    lbmp.LockBits();

    double y = -hsize;
    double yStep = hsize / (lbmp.Height / 2.0);
    double xStep = hsize / (lbmp.Width / 2.0);

    for (int i = 0; i < lbmp.Height; ++i)
    {
        double x = -hsize;

        for (int j = 0; j < lbmp.Width; ++j)
        {
            double g = gauss2d(x, y, std) * scale;

            g = g < 0.0 ? 0.0 : g;
            g = g > 255.0 ? 255.0 : g;

            lbmp.SetPixel(j, i, Color.FromArgb((int)g));

            x += xStep;
        }

        y += yStep;
    }

    lbmp.UnlockBits();

    return ComplexImage.FromBitmap(bmp);
}

private double gauss2d(double x, double y, double std)
{
    return (1.0 / (2 * Math.PI * std * std)) * Math.Exp(-(((x * x) + (y * y)) / (2 * std * std)));
}

private void applyGaussToImage(ComplexImage complexImage, ComplexImage filter)
{
    for (int i = 0; i < complexImage.Height; ++i)
    {
        for (int j = 0; j < complexImage.Width; ++j)
        {
            complexImage.Data[i, j] = AForge.Math.Complex.Multiply(complexImage.Data[i, j], filter.Data[i, j]);
        }
    }
}

将高斯模糊应用于频域图像

Applying Gaussian blur to image in frequency domain

c#

gaussian

aforge

emgucv

frequency-domain