# Fourier Transform (And Inverse) Of Images

I attended SIGGRAPH this year and there were some amazing talks.

There were quite a few talks dealing with the Fourier transform of images and sampling patterns, signal frequencies and bandwidth so I feel compelled to write up a blog post about the Fourier transform and inverse Fourier transform of images, as a transition to some other things that I want to write up.

At the bottom of this post is the source code to the program i used to make the examples. It’s a single CPP file that does not link to any libraries, and does not include any non standard headers (besides windows.h). It should be super simple to copy, paste, compile and use!

## Fourier Transform Overview

The Fourier transform converts data into the frequencies of sine and cosine waves that make up that data. Since we are going to be dealing with sampled data (pixels), we are going to be using the discrete Fourier transform.

After you perform the Fourier transform, you can run the inverse Fourier transform to get the original image back out.

You can also optionally modify the frequency data before running the inverse Fourier transform, which would give you an altered image as output.

In audio, a fourier transform is 1D, while with images, it’s 2D. That slows things down because a 1D Fourier transform is $O(N^2)$ while a 2D Fourier transform is $O(N^4)$.

This is quite an expensive operation as you can see, but there are some things that can mitigate the issue:

• The operation is separable on each axis. AKA only the naive implementation is $O(N^4)$.
• There is something called “The Fast Fourier Transform” which can make a 1D fourier transform go from $O(N^2)$ to $O(N log N)$ time complexity.
• Each pixel of output can be calculated without consideration of the other output pixels. The algorithm only needs read access to the source image or source data. This means that you can run this across however many cores you have on your CPU or GPU.

The items above are true of both the Fourier transform as well as the inverse Fourier transform.

The 2D Fourier transform takes a grid of REAL values as input of size MxN and returns a grid of COMPLEX values as output, also of size MxN.

The inverse 2D Fourier transform takes a grid of COMPLEX values as input, of size MxN and returns a grid of REAL values as output, also of size MxN.

The complex values are (of course!) made up of two components. You can get the amplitude of the frequency represented by the complex value by treating these components as a vector and getting the length. You can get the phase (angle that the frequency starts at) of the frequency by treating it like a vector and getting the angle it represents – like by using atan2(imaginary, real).

For more detailed information about the Fourier transform or the inverse Fourier transform, including the mathematical equations, please see the links at the end of this post!

## Image Examples

I’m going to show some examples of images that have been Fourier transformed, modified, and inverse Fourier transformed back. This should hopefully give you a more intuitive idea of what this stuff is all about.

I’m working with the source images in grey scale so i only have to work with one color channel (more on how to do that here: Blog.Demofox.Org: Converting RGB to Grayscale). You could easily do this same stuff with color images, but you would need to work with each color channel individually.

Zelda Guy

Here is the old man from “The Legend of Zelda” who gives you the sword. This image is 84×80 which takes about 1.75 seconds to do a fourier or inverse fourier transform with my naiive implementation of unoptimized code.

Taking a fourier transform of the greyscale version of that image gives me the following frequency amplitude (first) and phase (second) information. Note that we put the frequency amplitude through a log function to make the lesser represented frequencies show up more visibly.

Note that the center of the image represents frequency 0, aka DC. As you move out from the center, you get to higher and higher frequencies.

If you put that information into the inverse Fourier transform, you get the original image back out (in greyscale of course):

What if we changed the phase information though? Here’s what it looks like if we set all the frequencies to start at phase (angle) 0 instead of the proper angles, and then do an inverse Fourier transform:

It came out to be a completely different image! It has all the right frequencies, but the image is completely unrecognizable due to us messing with the phase data.

Interestingly, while your eyes are good at noticing differences in phase, your ears are not. That means that if this was a sound, instead of an image, you wouldn’t even be able to tell the difference. Strange isn’t it?

Now let’s do a low pass filter to our data. In other words, we are going to zero out the amplitude of all frequencies that are above a certain amount. We are going to zero out the frequencies that are farther than 10% of the image diagonal radius. That makes our frequency information look like this:

If we run the inverse Fourier transform on it, we get this:

The image got blurier because the high frequencies were removed. The high frequencies represent the small details of the image.

We also got some “ringing artifacts” which are the things that look like halos around the old man. This is also due to removing high frequency details. The short explanation for this is that it is very difficult to add sinusoids of different amplitudes and frequencies together to make a flat surface. To do so, you need a lot of small high frequency waves to fill in the areas next to the round hum humps to flatten it out. It’s the same issue you see when trying to make a square wave with additive synthesis, if you’ve read any of my posts on audio synthesis.

Now let’s try a high pass filter, where we remove frequencies that are closer than 10% of the image diagonal radius. First is the frequency amplitude information, and then the resulting image:

The results look pretty strange. These are the high frequency details that the blurry image is missing!

You could use a high pass filter on an image to do edge detection. You could use a low pass filter on an image to remove high frequency details before making the image smaller to prevent the aliasing that can happen when making an image smaller.

Let’s look at some other images that have been given similar treatment.

SIGGRAPH

Here’s a picture of myself at SIGGRAPH with my friend Paul who I used to work with at inXile! The image is 100×133 and takes about 6.5-7 seconds to do a Fourier transform or an inverse Fourier transform.

Here is the Fourier transform and inverse Fourier transform:

Here is the low pass frequency info and inverse Fourier transform:

Here is the high pass:

And here is the zero phase:

Simple Images

Lastly, here are some simple images, along with their frequency magnitude and phases. Sorry that they are so small, but hopefully you get the idea.

Horizontal Stripes:

Horizontal Stripe:

Vertical Stripes:

Vertical Stripe:

Diagonal Stripe:

You might notice that the Fourier transform frequency amplitudes actually run perpendicular to the orientation of the stripes. Look for a post soon which makes use of this property (:

## Example Code

Here is the code I used to generate the examples above.

If you pass this program the name of a 24 bit bmp file, it will generate and save the DFT, and also the inverse DFT to show that the image can survive a round trip. It will also do a low pass filter, high pass filter, and set the phase of all frequencies to zero, saving off both the frequency amplitude information, as well as the image generated from the frequency information for those operations.

The program below is written for clarity, not speed. In particular, the DFT and IDFT code is naively implemented so is O(N^4). To speed it up, it should be threaded, do the work on each axis separately, and also use a fast Fourier transform implementation.

#define _CRT_SECURE_NO_WARNINGS

#include <stdio.h>
#include <stdint.h>
#include <array>
#include <vector>
#include <complex>
#include <windows.h>  // for bitmap headers and performance counter.  Sorry non windows people!

const float c_pi = 3.14159265359f;
const float c_rootTwo = 1.41421356237f;

typedef uint8_t uint8;

struct SProgress
{
SProgress (const char* message, int total) : m_message(message), m_total(total)
{
m_amount = 0;
m_lastPercent = 0;
printf("%s   0%%", message);

QueryPerformanceFrequency(&m_freq);
QueryPerformanceCounter(&m_start);
}

~SProgress ()
{
// make it show 100%
m_amount = m_total;
Update(0);

// show how long it took
LARGE_INTEGER end;
QueryPerformanceCounter(&end);
printf(" (%0.2f seconds)n", seconds);
}

void Update (int delta = 1)
{
m_amount += delta;
int percent = int(100.0f * float(m_amount) / float(m_total));
if (percent <= m_lastPercent)
return;

m_lastPercent = percent;
printf("%c%c%c%c", 8, 8, 8, 8);
if (percent < 100)
printf(" ");
if (percent < 10)
printf(" ");
printf("%i%%", percent);
}

int m_lastPercent;
int m_amount;
int m_total;
const char* m_message;

LARGE_INTEGER m_start;
LARGE_INTEGER m_freq;
};

struct SImageData
{
SImageData ()
: m_width(0)
, m_height(0)
{ }

long m_width;
long m_height;
long m_pitch;
std::vector<uint8> m_pixels;
};

struct SImageDataComplex
{
SImageDataComplex ()
: m_width(0)
, m_height(0)
{ }

long m_width;
long m_height;
std::vector<std::complex<float>> m_pixels;
};

bool LoadImage (const char *fileName, SImageData& imageData)
{
// open the file if we can
FILE *file;
file = fopen(fileName, "rb");
if (!file)
return false;

{
fclose(file);
return false;
}

// read in our pixel data if we can. Note that it's in BGR order, and width is padded to the next power of 4
if (fread(&imageData.m_pixels[0], imageData.m_pixels.size(), 1, file) != 1)
{
fclose(file);
return false;
}

imageData.m_pitch = imageData.m_width*3;
if (imageData.m_pitch & 3)
{
imageData.m_pitch &= ~3;
imageData.m_pitch += 4;
}

fclose(file);
return true;
}

bool SaveImage (const char *fileName, const SImageData &image)
{
// open the file if we can
FILE *file;
file = fopen(fileName, "wb");
if (!file) {
printf("Could not save %sn", fileName);
return false;
}

// write the data and close the file
fclose(file);

printf("%s savedn", fileName);
return true;
}

void ImageToGrey (const SImageData &srcImage, SImageData &destImage)
{
destImage = srcImage;

for (int x = 0; x < srcImage.m_width; ++x)
{
for (int y = 0; y < srcImage.m_height; ++y)
{
const uint8 *src = &srcImage.m_pixels[(y * srcImage.m_pitch) + x * 3];
uint8 *dest = &destImage.m_pixels[(y * destImage.m_pitch) + x * 3];

uint8 grey = uint8((float(src[0]) * 0.3f + float(src[1]) * 0.59f + float(src[2]) * 0.11f));
dest[0] = grey;
dest[1] = grey;
dest[2] = grey;
}
}
}

std::complex<float> DFTPixel (const SImageData &srcImage, int K, int L)
{
std::complex<float> ret(0.0f, 0.0f);

for (int x = 0; x < srcImage.m_width; ++x)
{
for (int y = 0; y < srcImage.m_height; ++y)
{
// Get the pixel value (assuming greyscale) and convert it to [0,1] space
const uint8 *src = &srcImage.m_pixels[(y * srcImage.m_pitch) + x * 3];
float grey = float(src[0]) / 255.0f;

// Add to the sum of the return value
float v = float(K * x) / float(srcImage.m_width);
v += float(L * y) / float(srcImage.m_height);
ret += std::complex<float>(grey, 0.0f) * std::polar<float>(1.0f, -2.0f * c_pi * v);
}
}

return ret;
}

void DFTImage (const SImageData &srcImage, SImageDataComplex &destImage)
{
// NOTE: this function assumes srcImage is greyscale, so works on only the red component of srcImage.
// ImageToGrey() will convert an image to greyscale.

// size the output dft data
destImage.m_width = srcImage.m_width;
destImage.m_height = srcImage.m_height;
destImage.m_pixels.resize(destImage.m_width*destImage.m_height);

SProgress progress("DFT:", srcImage.m_width * srcImage.m_height);

// calculate 2d dft (brute force, not using fast fourier transform)
for (int x = 0; x < srcImage.m_width; ++x)
{
for (int y = 0; y < srcImage.m_height; ++y)
{
// calculate DFT for that pixel / frequency
destImage.m_pixels[y * destImage.m_width + x] = DFTPixel(srcImage, x, y);

// update progress
progress.Update();
}
}
}

uint8 InverseDFTPixel (const SImageDataComplex &srcImage, int K, int L)
{
std::complex<float> total(0.0f, 0.0f);
for (int x = 0; x < srcImage.m_width; ++x)
{
for (int y = 0; y < srcImage.m_height; ++y)
{
// Get the pixel value
const std::complex<float> &src = srcImage.m_pixels[(y * srcImage.m_width) + x];

// Add to the sum of the return value
float v = float(K * x) / float(srcImage.m_width);
v += float(L * y) / float(srcImage.m_height);
std::complex<float> result = src * std::polar<float>(1.0f, 2.0f * c_pi * v);

// sum up the results
total += result;
}
}

float idft = std::abs(total) / float(srcImage.m_width*srcImage.m_height);

// make sure the values are in range
if (idft < 0.0f)
idft = 0.0f;
if (idft > 1.0f)
idft = 1.0;

return uint8(idft * 255.0f);
}

void InverseDFTImage (const SImageDataComplex &srcImage, SImageData &destImage)
{
// size the output image
destImage.m_width = srcImage.m_width;
destImage.m_height = srcImage.m_height;
destImage.m_pitch = srcImage.m_width * 3;
if (destImage.m_pitch & 3)
{
destImage.m_pitch &= ~3;
destImage.m_pitch += 4;
}
destImage.m_pixels.resize(destImage.m_pitch*destImage.m_height);

SProgress progress("Inverse DFT:", srcImage.m_width*srcImage.m_height);

// calculate inverse 2d dft (brute force, not using fast fourier transform)
for (int x = 0; x < srcImage.m_width; ++x)
{
for (int y = 0; y < srcImage.m_height; ++y)
{
// calculate DFT for that pixel / frequency
uint8 idft = InverseDFTPixel(srcImage, x, y);
uint8* dest = &destImage.m_pixels[y*destImage.m_pitch + x * 3];
dest[0] = idft;
dest[1] = idft;
dest[2] = idft;

// update progress
progress.Update();
}
}
}

void GetMagnitudeData (const SImageDataComplex& srcImage, SImageData& destImage)
{
// size the output image
destImage.m_width = srcImage.m_width;
destImage.m_height = srcImage.m_height;
destImage.m_pitch = srcImage.m_width * 3;
if (destImage.m_pitch & 3)
{
destImage.m_pitch &= ~3;
destImage.m_pitch += 4;
}
destImage.m_pixels.resize(destImage.m_pitch*destImage.m_height);

// get floating point magnitude data
std::vector<float> magArray;
magArray.resize(srcImage.m_width*srcImage.m_height);
float maxmag = 0.0f;
for (int x = 0; x < srcImage.m_width; ++x)
{
for (int y = 0; y < srcImage.m_height; ++y)
{
// Offset the information by half width & height in the positive direction.
// This makes frequency 0 (DC) be at the image origin, like most diagrams show it.
int k = (x + srcImage.m_width / 2) % srcImage.m_width;
int l = (y + srcImage.m_height / 2) % srcImage.m_height;
const std::complex<float> &src = srcImage.m_pixels[l*srcImage.m_width + k];

float mag = std::abs(src);
if (mag > maxmag)
maxmag = mag;

magArray[y*srcImage.m_width + x] = mag;
}
}
if (maxmag == 0.0f)
maxmag = 1.0f;

const float c = 255.0f / log(1.0f+maxmag);

// normalize the magnitude data and send it back in [0, 255]
for (int x = 0; x < srcImage.m_width; ++x)
{
for (int y = 0; y < srcImage.m_height; ++y)
{
float src = c * log(1.0f + magArray[y*srcImage.m_width + x]);

uint8 magu8 = uint8(src);

uint8* dest = &destImage.m_pixels[y*destImage.m_pitch + x * 3];
dest[0] = magu8;
dest[1] = magu8;
dest[2] = magu8;
}
}
}

void GetPhaseData (const SImageDataComplex& srcImage, SImageData& destImage)
{
// size the output image
destImage.m_width = srcImage.m_width;
destImage.m_height = srcImage.m_height;
destImage.m_pitch = srcImage.m_width * 3;
if (destImage.m_pitch & 3)
{
destImage.m_pitch &= ~3;
destImage.m_pitch += 4;
}
destImage.m_pixels.resize(destImage.m_pitch*destImage.m_height);

// get floating point phase data, and encode it in [0,255]
for (int x = 0; x < srcImage.m_width; ++x)
{
for (int y = 0; y < srcImage.m_height; ++y)
{
// Offset the information by half width & height in the positive direction.
// This makes frequency 0 (DC) be at the image origin, like most diagrams show it.
int k = (x + srcImage.m_width / 2) % srcImage.m_width;
int l = (y + srcImage.m_height / 2) % srcImage.m_height;
const std::complex<float> &src = srcImage.m_pixels[l*srcImage.m_width + k];

// get phase, and change it from [-pi,+pi] to [0,255]
float phase = (0.5f + 0.5f * std::atan2(src.real(), src.imag()) / c_pi);
if (phase < 0.0f)
phase = 0.0f;
if (phase > 1.0f)
phase = 1.0;
uint8 phase255 = uint8(phase * 255);

// write the phase as grey scale color
uint8* dest = &destImage.m_pixels[y*destImage.m_pitch + x * 3];
dest[0] = phase255;
dest[1] = phase255;
dest[2] = phase255;
}
}
}

int main (int argc, char **argv)
{
float scale = 1.0f;
int filter = 0;

bool showUsage = argc < 2;
char *srcFileName = argv[1];

if (showUsage)
{
printf("Usage: <source>nn");
return 1;
}

// trim off file extension from source filename so we can make our other file names
char baseFileName[1024];
strcpy(baseFileName, srcFileName);
for (int i = strlen(baseFileName) - 1; i >= 0; --i)
{
if (baseFileName[i] == '.')
{
baseFileName[i] = 0;
break;
}
}

// Load source image if we can
SImageData srcImage;
{
printf("%s loaded (%i x %i)n", srcFileName, srcImage.m_width, srcImage.m_height);

// do DFT on a greyscale version of the image, instead of doing it per color channel
SImageData greyImage;
ImageToGrey(srcImage, greyImage);
SImageDataComplex frequencyData;
DFTImage(greyImage, frequencyData);

// save magnitude information
{
char outFileName[1024];
strcpy(outFileName, baseFileName);
strcat(outFileName, ".raw.mag.bmp");

SImageData destImage;
GetMagnitudeData(frequencyData, destImage);
SaveImage(outFileName, destImage);
}

// save phase information
{
char outFileName[1024];
strcpy(outFileName, baseFileName);
strcat(outFileName, ".raw.phase.bmp");

SImageData destImage;
GetPhaseData(frequencyData, destImage);
SaveImage(outFileName, destImage);
}

// inverse dft the modified frequency and save the result
{
char outFileName[1024];
strcpy(outFileName, baseFileName);
strcat(outFileName, ".raw.idft.bmp");

SImageData modifiedImage;
InverseDFTImage(frequencyData, modifiedImage);
SaveImage(outFileName, modifiedImage);
}

// Low Pass Filter: Remove high frequencies, write out frequency magnitudes, write out inverse dft
{
printf("n=====LPF=====n");

// remove frequencies that are too far from frequency 0.
// Note that even though our output frequency images have frequency 0 (DC) in the center, that
// isn't actually how it's stored in our SImageDataComplex structure.  Pixel (0,0) is frequency 0.
SImageDataComplex dft = frequencyData;
float halfWidth = float(dft.m_width / 2);
float halfHeight = float(dft.m_height / 2);
for (int x = 0; x < dft.m_width; ++x)
{
for (int y = 0; y < dft.m_height; ++y)
{
float relX = 0.0f;
float relY = 0.0f;

if (x < halfWidth)
relX = float(x) / halfWidth;
else
relX = (float(x) - float(dft.m_width)) / halfWidth;

if (y < halfHeight)
relY = float(y) / halfHeight;
else
relY = (float(y) - float(dft.m_height)) / halfHeight;

float dist = sqrt(relX*relX + relY*relY) / c_rootTwo; // divided by root 2 so our distance is from 0 to 1
if (dist > 0.1f)
dft.m_pixels[y*dft.m_width + x] = std::complex<float>(0.0f, 0.0f);
}
}

// write dft magnitude data
char outFileName[1024];
strcpy(outFileName, baseFileName);
strcat(outFileName, ".lpf.mag.bmp");
SImageData destImage;
GetMagnitudeData(dft, destImage);
SaveImage(outFileName, destImage);

// inverse dft and save the image
strcpy(outFileName, baseFileName);
strcat(outFileName, ".lpf.idft.bmp");
SImageData modifiedImage;
InverseDFTImage(dft, modifiedImage);
SaveImage(outFileName, modifiedImage);
}

// High Pass Filter: Remove low frequencies, write out frequency magnitudes, write out inverse dft
{
printf("n=====HPF=====n");

// remove frequencies that are too close to frequency 0.
// Note that even though our output frequency images have frequency 0 (DC) in the center, that
// isn't actually how it's stored in our SImageDataComplex structure.  Pixel (0,0) is frequency 0.
SImageDataComplex dft = frequencyData;
float halfWidth = float(dft.m_width / 2);
float halfHeight = float(dft.m_height / 2);
for (int x = 0; x < dft.m_width; ++x)
{
for (int y = 0; y < dft.m_height; ++y)
{
float relX = 0.0f;
float relY = 0.0f;

if (x < halfWidth)
relX = float(x) / halfWidth;
else
relX = (float(x) - float(dft.m_width)) / halfWidth;

if (y < halfHeight)
relY = float(y) / halfHeight;
else
relY = (float(y) - float(dft.m_height)) / halfHeight;

float dist = sqrt(relX*relX + relY*relY) / c_rootTwo; // divided by root 2 so our distance is from 0 to 1
if (dist < 0.1f)
dft.m_pixels[y*dft.m_width + x] = std::complex<float>(0.0f, 0.0f);
}
}

// write dft magnitude data
char outFileName[1024];
strcpy(outFileName, baseFileName);
strcat(outFileName, ".hpf.mag.bmp");
SImageData destImage;
GetMagnitudeData(dft, destImage);
SaveImage(outFileName, destImage);

// inverse dft and save the image
strcpy(outFileName, baseFileName);
strcat(outFileName, ".hpf.idft.bmp");
SImageData modifiedImage;
InverseDFTImage(dft, modifiedImage);
SaveImage(outFileName, modifiedImage);
}

// ZeroPhase
{
printf("n=====Zero Phase=====n");

// Set phase to zero for all frequencies.
// Note that even though our output frequency images have frequency 0 (DC) in the center, that
// isn't actually how it's stored in our SImageDataComplex structure.  Pixel (0,0) is frequency 0.
SImageDataComplex dft = frequencyData;
float halfWidth = float(dft.m_width / 2);
float halfHeight = float(dft.m_height / 2);
for (int x = 0; x < dft.m_width; ++x)
{
for (int y = 0; y < dft.m_height; ++y)
{
std::complex<float>& v = dft.m_pixels[y*dft.m_width + x];
float mag = std::abs(v);
v = std::complex<float>(mag, 0.0f);
}
}

// write dft magnitude data
char outFileName[1024];
strcpy(outFileName, baseFileName);
strcat(outFileName, ".phase0.mag.bmp");
SImageData destImage;
GetMagnitudeData(dft, destImage);
SaveImage(outFileName, destImage);

// inverse dft and save the image
strcpy(outFileName, baseFileName);
strcat(outFileName, ".phase0.idft.bmp");
SImageData modifiedImage;
InverseDFTImage(dft, modifiedImage);
SaveImage(outFileName, modifiedImage);
}
}
else
printf("could not read 24 bit bmp file %snn", srcFileName);

return 0;
}

Here are some links that I found useful:
Fourier Transform
http://www.thefouriertransform.com/
Introduction To Fourier Transforms For Image Processing

# Intro To Audio Synthesis For Music Presentation

Today I gave a presentation at work on the basics of audio synthesis for music. It seemed to go fairly well and I was surprised to hear that so many others also dabbled in audio synth and music.

The slide deck and example C++ program (uses portaudio) are both up on github here:
https://github.com/Atrix256/MusicSynth

Questions, feedback, etc? Drop me a line (: