我已经在频域中将高斯模糊应用于图像。 由于未知原因(可能我没有错)我收到有线图像而不是模糊的图像。
我的目标是一步一步:
将图像拆分为不同的通道。
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();
}
将每个频道转换为复杂的图像(使用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(为了测试目的,内核大小等于图像大小,只有中心部分用高斯函数计算,其余内核等于re = 1 im = 0)。 / p>
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))));
}
将FFT应用于每个通道和内核。
通过内核将每个通道的中心部分相乘。
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]);
}
}
}
将每个频道转换回位图(使用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,我已经移动了红色模糊的图像版本:link。
@edit - 通过对代码进行了一些更改来更新问题。
答案 0 :(得分:0)
我在DSP stackexchange上得到了一些帮助......还有一些作弊但它有效。主要问题是内核生成和应用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Bitmap期间乘以255(感谢Olli Niemitalo @ DSP SE)。
我是如何解决这个问题的:
但是我不能在生成的滤波器上使用FFT,因为生成的滤波器看起来像FFT后的滤波器。它的工作原理 - 输出图像模糊,没有伪影,所以我认为这不是太糟糕。
图像(我不能发布超过2个链接,图像非常大):
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]);
}
}
}