patterncsharpMinor
FFT Convolution
Viewed 0 times
convolutionfftstackoverflow
Problem
I have written the following routines to convolve two images in the frequency domain which are represented as 2d Complex arrays.
How can I optimize my routines for better performance?
```
public static class Convolution
{
public static Complex[,] Convolve(Complex[,] image, Complex[,] mask)
{
Complex[,] convolve = null;
int imageWidth = image.GetLength(0);
int imageHeight = image.GetLength(1);
int maskWidth = mask.GetLength(0);
int maskeHeight = mask.GetLength(1);
if (imageWidth == maskWidth && imageHeight == maskeHeight)
{
FourierTransform ftForImage = new FourierTransform(image); ftForImage.ForwardFFT();
FourierTransform ftForMask = new FourierTransform(mask); ftForMask.ForwardFFT();
Complex[,] fftImage = ftForImage.FourierImageComplex;
Complex[,] fftKernel = ftForMask.FourierImageComplex;
Complex[,] fftConvolved = new Complex[imageWidth, imageHeight];
for (int j = 0; j < imageHeight; j++)
{
for (int i = 0; i < imageWidth; i++)
{
fftConvolved[i, j] = fftImage[i, j] * fftKernel[i, j];
}
}
FourierTransform ftForConv = new FourierTransform();
ftForConv.InverseFFT(fftConvolved);
convolve = ftForConv.GrayscaleImageComplex;
Rescale(convolve);
convolve = FourierShifter.FFTShift(convolve);
}
else
{
throw new Exception("padding needed");
}
return convolve;
}
//Rescale values between 0 and 255.
private static void Rescale(Complex[,] convolve)
{
int imageWidth = convolve.GetLength(0);
int imageHeight = convolve.GetLength(1);
double maxAmp = 0.0;
for (int j = 0; j < imageHeight; j++)
{
for (int i = 0; i < imageWidth; i++)
{
maxAm
How can I optimize my routines for better performance?
```
public static class Convolution
{
public static Complex[,] Convolve(Complex[,] image, Complex[,] mask)
{
Complex[,] convolve = null;
int imageWidth = image.GetLength(0);
int imageHeight = image.GetLength(1);
int maskWidth = mask.GetLength(0);
int maskeHeight = mask.GetLength(1);
if (imageWidth == maskWidth && imageHeight == maskeHeight)
{
FourierTransform ftForImage = new FourierTransform(image); ftForImage.ForwardFFT();
FourierTransform ftForMask = new FourierTransform(mask); ftForMask.ForwardFFT();
Complex[,] fftImage = ftForImage.FourierImageComplex;
Complex[,] fftKernel = ftForMask.FourierImageComplex;
Complex[,] fftConvolved = new Complex[imageWidth, imageHeight];
for (int j = 0; j < imageHeight; j++)
{
for (int i = 0; i < imageWidth; i++)
{
fftConvolved[i, j] = fftImage[i, j] * fftKernel[i, j];
}
}
FourierTransform ftForConv = new FourierTransform();
ftForConv.InverseFFT(fftConvolved);
convolve = ftForConv.GrayscaleImageComplex;
Rescale(convolve);
convolve = FourierShifter.FFTShift(convolve);
}
else
{
throw new Exception("padding needed");
}
return convolve;
}
//Rescale values between 0 and 255.
private static void Rescale(Complex[,] convolve)
{
int imageWidth = convolve.GetLength(0);
int imageHeight = convolve.GetLength(1);
double maxAmp = 0.0;
for (int j = 0; j < imageHeight; j++)
{
for (int i = 0; i < imageWidth; i++)
{
maxAm
Solution
A few detail remarks:
This variable declaration should be moved further down, to the line where it is actually needed. Initializing it with
Since
In the second loop,
Complex[,] convolve = null;This variable declaration should be moved further down, to the line where it is actually needed. Initializing it with
null is misleading.maxAmp = Math.Max(maxAmp, convolve[i, j].Magnitude);Since
Magnitude involves a square root, it is expensive to calculate. Prefer calculating the square magnitude only and calculate the square root only once at the end. This saves width * height - 1 square root calculations.In the second loop,
maxAmp is calculated again but never used. Removing it saves another width * height calculations.Code Snippets
Complex[,] convolve = null;maxAmp = Math.Max(maxAmp, convolve[i, j].Magnitude);Context
StackExchange Code Review Q#139860, answer score: 3
Revisions (0)
No revisions yet.