Calculating the first derivative of an image using DFT
$begingroup$
I need to calculate the first derivative of a greyscale image (a 2D array) using a DFT function I built (which works). Unfortunately, the results don't seem to be correct.
In the fourier domain, the derivative d/dx is given as F(u,v)* 2*pi*i/N * u, where u is the x-axis transformed, N is the size of one of the matrix's dimensions, v being the other one.
Attached is the code. What bothers me is that I'm not getting the same results as I would differentiating by convolution with (1,-1) or (1,-1) as a column vector.
def derivative(fourier_signal):
"""
Derivative in fourier domain is multiplying by u or v, and 2pi*i/N
:param fourier_signal:
:return:
"""
N = np.shape(fourier_signal)[ZERO]
M = np.shape(fourier_signal)[ONE]
u = np.arange(N)
v = np.arange(M)
du = fourier_signal * (u*TWO_PI*1j)/N
dv = fourier_signal * (v*TWO_PI*1j)/M
return du, dv
def fourier_der(im):
# Calculate DFT2
dft_image = DFT2(im)
# Function that Multiply by rows by u, columns by y
du, dv = derivative(dft_image)
shifted_du, shifted_dv = np.fft.fftshift(du), np.fft.fftshift(dv)
dx, dy = IDFT2(shifted_du), IDFT2(shifted_dv)
I'm not looking for easy answers on how to do it, but rather a direction to as to why my output is incorrect.
dft python
asked Nov 13 '18 at 22:31
RonaldBRonaldB
1122
$endgroup$
add a comment |
$begingroup$
I need to calculate the first derivative of a greyscale image (a 2D array) using a DFT function I built (which works). Unfortunately, the results don't seem to be correct.
In the fourier domain, the derivative d/dx is given as F(u,v)* 2*pi*i/N * u, where u is the x-axis transformed, N is the size of one of the matrix's dimensions, v being the other one.
Attached is the code. What bothers me is that I'm not getting the same results as I would differentiating by convolution with (1,-1) or (1,-1) as a column vector.
def derivative(fourier_signal):
"""
Derivative in fourier domain is multiplying by u or v, and 2pi*i/N
:param fourier_signal:
:return:
"""
N = np.shape(fourier_signal)[ZERO]
M = np.shape(fourier_signal)[ONE]
u = np.arange(N)
v = np.arange(M)
du = fourier_signal * (u*TWO_PI*1j)/N
dv = fourier_signal * (v*TWO_PI*1j)/M
return du, dv
def fourier_der(im):
# Calculate DFT2
dft_image = DFT2(im)
# Function that Multiply by rows by u, columns by y
du, dv = derivative(dft_image)
shifted_du, shifted_dv = np.fft.fftshift(du), np.fft.fftshift(dv)
dx, dy = IDFT2(shifted_du), IDFT2(shifted_dv)
I'm not looking for easy answers on how to do it, but rather a direction to as to why my output is incorrect.
dft python
asked Nov 13 '18 at 22:31
RonaldBRonaldB
1122
$endgroup$
$begingroup$
1) Aliasing. The ideal derivative filter you're using in the frequency domain is infinitely long in the time domain. You're getting aliases in the time domain. 2) [1, -1] as filter taps is a scaled truncation of the ideal derivative filter. It is in error at high frequencies. So in light of 1 & 2, you should really specify over which frequencies how much error you can tolerate. Then you can build a filter to that specification.
$endgroup$
– Andy Walls
Nov 13 '18 at 22:51
$begingroup$
BTW, GNURadio has a little stand alone utility for building Minimum Mean Squared Error differentiating FIR filter taps. GNURadio uses it to build a polyphase filter bank for an interpolating differentiator, with 8 taps in each polyphase filter arm, and MMSE over the frequencies in the interval $[-f_s/4, f_s/4]$. github.com/gnuradio/gnuradio/tree/master/gr-filter/lib/…
$endgroup$
– Andy Walls
Nov 13 '18 at 23:08
add a comment |
$begingroup$
I need to calculate the first derivative of a greyscale image (a 2D array) using a DFT function I built (which works). Unfortunately, the results don't seem to be correct.
In the fourier domain, the derivative d/dx is given as F(u,v)* 2*pi*i/N * u, where u is the x-axis transformed, N is the size of one of the matrix's dimensions, v being the other one.
Attached is the code. What bothers me is that I'm not getting the same results as I would differentiating by convolution with (1,-1) or (1,-1) as a column vector.
def derivative(fourier_signal):
"""
Derivative in fourier domain is multiplying by u or v, and 2pi*i/N
:param fourier_signal:
:return:
"""
N = np.shape(fourier_signal)[ZERO]
M = np.shape(fourier_signal)[ONE]
u = np.arange(N)
v = np.arange(M)
du = fourier_signal * (u*TWO_PI*1j)/N
dv = fourier_signal * (v*TWO_PI*1j)/M
return du, dv
def fourier_der(im):
# Calculate DFT2
dft_image = DFT2(im)
# Function that Multiply by rows by u, columns by y
du, dv = derivative(dft_image)
shifted_du, shifted_dv = np.fft.fftshift(du), np.fft.fftshift(dv)
dx, dy = IDFT2(shifted_du), IDFT2(shifted_dv)
I'm not looking for easy answers on how to do it, but rather a direction to as to why my output is incorrect.
dft python
asked Nov 13 '18 at 22:31
RonaldBRonaldB
1122
$endgroup$
I need to calculate the first derivative of a greyscale image (a 2D array) using a DFT function I built (which works). Unfortunately, the results don't seem to be correct.
In the fourier domain, the derivative d/dx is given as F(u,v)* 2*pi*i/N * u, where u is the x-axis transformed, N is the size of one of the matrix's dimensions, v being the other one.
Attached is the code. What bothers me is that I'm not getting the same results as I would differentiating by convolution with (1,-1) or (1,-1) as a column vector.
def derivative(fourier_signal):
"""
Derivative in fourier domain is multiplying by u or v, and 2pi*i/N
:param fourier_signal:
:return:
"""
N = np.shape(fourier_signal)[ZERO]
M = np.shape(fourier_signal)[ONE]
u = np.arange(N)
v = np.arange(M)
du = fourier_signal * (u*TWO_PI*1j)/N
dv = fourier_signal * (v*TWO_PI*1j)/M
return du, dv
def fourier_der(im):
# Calculate DFT2
dft_image = DFT2(im)
# Function that Multiply by rows by u, columns by y
du, dv = derivative(dft_image)
shifted_du, shifted_dv = np.fft.fftshift(du), np.fft.fftshift(dv)
dx, dy = IDFT2(shifted_du), IDFT2(shifted_dv)
I'm not looking for easy answers on how to do it, but rather a direction to as to why my output is incorrect.
dft python
dft python
asked Nov 13 '18 at 22:31
RonaldBRonaldB
1122
asked Nov 13 '18 at 22:31
RonaldBRonaldB
1122
asked Nov 13 '18 at 22:31
RonaldBRonaldB
1122
asked Nov 13 '18 at 22:31
RonaldBRonaldB
1122
asked Nov 13 '18 at 22:31
RonaldBRonaldB
1122
1122
$begingroup$
1) Aliasing. The ideal derivative filter you're using in the frequency domain is infinitely long in the time domain. You're getting aliases in the time domain. 2) [1, -1] as filter taps is a scaled truncation of the ideal derivative filter. It is in error at high frequencies. So in light of 1 & 2, you should really specify over which frequencies how much error you can tolerate. Then you can build a filter to that specification.
$endgroup$
– Andy Walls
Nov 13 '18 at 22:51
$begingroup$
BTW, GNURadio has a little stand alone utility for building Minimum Mean Squared Error differentiating FIR filter taps. GNURadio uses it to build a polyphase filter bank for an interpolating differentiator, with 8 taps in each polyphase filter arm, and MMSE over the frequencies in the interval $[-f_s/4, f_s/4]$. github.com/gnuradio/gnuradio/tree/master/gr-filter/lib/…
$endgroup$
– Andy Walls
Nov 13 '18 at 23:08
add a comment |
$begingroup$
1) Aliasing. The ideal derivative filter you're using in the frequency domain is infinitely long in the time domain. You're getting aliases in the time domain. 2) [1, -1] as filter taps is a scaled truncation of the ideal derivative filter. It is in error at high frequencies. So in light of 1 & 2, you should really specify over which frequencies how much error you can tolerate. Then you can build a filter to that specification.
$endgroup$
– Andy Walls
Nov 13 '18 at 22:51
$begingroup$
BTW, GNURadio has a little stand alone utility for building Minimum Mean Squared Error differentiating FIR filter taps. GNURadio uses it to build a polyphase filter bank for an interpolating differentiator, with 8 taps in each polyphase filter arm, and MMSE over the frequencies in the interval $[-f_s/4, f_s/4]$. github.com/gnuradio/gnuradio/tree/master/gr-filter/lib/…
$endgroup$
– Andy Walls
Nov 13 '18 at 23:08
$begingroup$
1) Aliasing. The ideal derivative filter you're using in the frequency domain is infinitely long in the time domain. You're getting aliases in the time domain. 2) [1, -1] as filter taps is a scaled truncation of the ideal derivative filter. It is in error at high frequencies. So in light of 1 & 2, you should really specify over which frequencies how much error you can tolerate. Then you can build a filter to that specification.
$endgroup$
– Andy Walls
Nov 13 '18 at 22:51
$begingroup$
1) Aliasing. The ideal derivative filter you're using in the frequency domain is infinitely long in the time domain. You're getting aliases in the time domain. 2) [1, -1] as filter taps is a scaled truncation of the ideal derivative filter. It is in error at high frequencies. So in light of 1 & 2, you should really specify over which frequencies how much error you can tolerate. Then you can build a filter to that specification.
$endgroup$
– Andy Walls
Nov 13 '18 at 22:51
$begingroup$
BTW, GNURadio has a little stand alone utility for building Minimum Mean Squared Error differentiating FIR filter taps. GNURadio uses it to build a polyphase filter bank for an interpolating differentiator, with 8 taps in each polyphase filter arm, and MMSE over the frequencies in the interval $[-f_s/4, f_s/4]$. github.com/gnuradio/gnuradio/tree/master/gr-filter/lib/…
$endgroup$
– Andy Walls
Nov 13 '18 at 23:08
$begingroup$
BTW, GNURadio has a little stand alone utility for building Minimum Mean Squared Error differentiating FIR filter taps. GNURadio uses it to build a polyphase filter bank for an interpolating differentiator, with 8 taps in each polyphase filter arm, and MMSE over the frequencies in the interval $[-f_s/4, f_s/4]$. github.com/gnuradio/gnuradio/tree/master/gr-filter/lib/…
$endgroup$
– Andy Walls
Nov 13 '18 at 23:08
add a comment |
1 Answer
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They are not the same. Using 1D notation,
the discrete-time (backward) first difference is $x[n] - x[n-1]$ whose frequency domain DTFT equivalent is
$$ x[n]-x[n-1] leftrightarrow X(e^jomega) - e^-j omega X(e^jomega) =X(e^jomega)(1- e^-j omega) $$
which becomes
$$ x[n]-x[n-1] longleftrightarrow X[k](1 - e^-j frac2 piN k)$$ using the DFT to implement it.
The FIR impulse response of the discrete-time system that implements the first difference is therefore,
$$ h[n] = delta[n] - delta[n-1]$$
The first derivative of a continuous-variable function $x(t)$ is $x'(
t)$ and in CTFT domain it becomes :
$$ x'(t) longleftrightarrow jOmega X(Omega) $$
where the analog system frequency response is
$$H_d(Omega) = j Omega $$
which is not implementable in digital form, but a bandlimited approximation to it is attained under a sampling period of $T$ that yields an equivalent discrete-time frequency response of a (bandlimited) differentiator as
$$ H_d(e^j omega) = j fracomegaT ~~~, ~~~~ |omega| < pi$$
The associated discrete-time (IIR) impulse response is
$$ h_d[n] = textIDTFT H_d(e^j omega) = textIDTFT j fracomegaT $$
Practically you would truncate and window $h_d[n]$ before using.
So they are not the same.
answered Nov 13 '18 at 23:08
Fat32Fat32
14.7k31229
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add a comment |
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$begingroup$
They are not the same. Using 1D notation,
the discrete-time (backward) first difference is $x[n] - x[n-1]$ whose frequency domain DTFT equivalent is
$$ x[n]-x[n-1] leftrightarrow X(e^jomega) - e^-j omega X(e^jomega) =X(e^jomega)(1- e^-j omega) $$
which becomes
$$ x[n]-x[n-1] longleftrightarrow X[k](1 - e^-j frac2 piN k)$$ using the DFT to implement it.
The FIR impulse response of the discrete-time system that implements the first difference is therefore,
$$ h[n] = delta[n] - delta[n-1]$$
The first derivative of a continuous-variable function $x(t)$ is $x'(
t)$ and in CTFT domain it becomes :
$$ x'(t) longleftrightarrow jOmega X(Omega) $$
where the analog system frequency response is
$$H_d(Omega) = j Omega $$
which is not implementable in digital form, but a bandlimited approximation to it is attained under a sampling period of $T$ that yields an equivalent discrete-time frequency response of a (bandlimited) differentiator as
$$ H_d(e^j omega) = j fracomegaT ~~~, ~~~~ |omega| < pi$$
The associated discrete-time (IIR) impulse response is
$$ h_d[n] = textIDTFT H_d(e^j omega) = textIDTFT j fracomegaT $$
Practically you would truncate and window $h_d[n]$ before using.
So they are not the same.
answered Nov 13 '18 at 23:08
Fat32Fat32
14.7k31229
$endgroup$
add a comment |
$begingroup$
They are not the same. Using 1D notation,
the discrete-time (backward) first difference is $x[n] - x[n-1]$ whose frequency domain DTFT equivalent is
$$ x[n]-x[n-1] leftrightarrow X(e^jomega) - e^-j omega X(e^jomega) =X(e^jomega)(1- e^-j omega) $$
which becomes
$$ x[n]-x[n-1] longleftrightarrow X[k](1 - e^-j frac2 piN k)$$ using the DFT to implement it.
The FIR impulse response of the discrete-time system that implements the first difference is therefore,
$$ h[n] = delta[n] - delta[n-1]$$
The first derivative of a continuous-variable function $x(t)$ is $x'(
t)$ and in CTFT domain it becomes :
$$ x'(t) longleftrightarrow jOmega X(Omega) $$
where the analog system frequency response is
$$H_d(Omega) = j Omega $$
which is not implementable in digital form, but a bandlimited approximation to it is attained under a sampling period of $T$ that yields an equivalent discrete-time frequency response of a (bandlimited) differentiator as
$$ H_d(e^j omega) = j fracomegaT ~~~, ~~~~ |omega| < pi$$
The associated discrete-time (IIR) impulse response is
$$ h_d[n] = textIDTFT H_d(e^j omega) = textIDTFT j fracomegaT $$
Practically you would truncate and window $h_d[n]$ before using.
So they are not the same.
answered Nov 13 '18 at 23:08
Fat32Fat32
14.7k31229
$endgroup$
add a comment |
$begingroup$
They are not the same. Using 1D notation,
the discrete-time (backward) first difference is $x[n] - x[n-1]$ whose frequency domain DTFT equivalent is
$$ x[n]-x[n-1] leftrightarrow X(e^jomega) - e^-j omega X(e^jomega) =X(e^jomega)(1- e^-j omega) $$
which becomes
$$ x[n]-x[n-1] longleftrightarrow X[k](1 - e^-j frac2 piN k)$$ using the DFT to implement it.
The FIR impulse response of the discrete-time system that implements the first difference is therefore,
$$ h[n] = delta[n] - delta[n-1]$$
The first derivative of a continuous-variable function $x(t)$ is $x'(
t)$ and in CTFT domain it becomes :
$$ x'(t) longleftrightarrow jOmega X(Omega) $$
where the analog system frequency response is
$$H_d(Omega) = j Omega $$
which is not implementable in digital form, but a bandlimited approximation to it is attained under a sampling period of $T$ that yields an equivalent discrete-time frequency response of a (bandlimited) differentiator as
$$ H_d(e^j omega) = j fracomegaT ~~~, ~~~~ |omega| < pi$$
The associated discrete-time (IIR) impulse response is
$$ h_d[n] = textIDTFT H_d(e^j omega) = textIDTFT j fracomegaT $$
Practically you would truncate and window $h_d[n]$ before using.
So they are not the same.
answered Nov 13 '18 at 23:08
Fat32Fat32
14.7k31229
$endgroup$
They are not the same. Using 1D notation,
the discrete-time (backward) first difference is $x[n] - x[n-1]$ whose frequency domain DTFT equivalent is
$$ x[n]-x[n-1] leftrightarrow X(e^jomega) - e^-j omega X(e^jomega) =X(e^jomega)(1- e^-j omega) $$
which becomes
$$ x[n]-x[n-1] longleftrightarrow X[k](1 - e^-j frac2 piN k)$$ using the DFT to implement it.
The FIR impulse response of the discrete-time system that implements the first difference is therefore,
$$ h[n] = delta[n] - delta[n-1]$$
The first derivative of a continuous-variable function $x(t)$ is $x'(
t)$ and in CTFT domain it becomes :
$$ x'(t) longleftrightarrow jOmega X(Omega) $$
where the analog system frequency response is
$$H_d(Omega) = j Omega $$
which is not implementable in digital form, but a bandlimited approximation to it is attained under a sampling period of $T$ that yields an equivalent discrete-time frequency response of a (bandlimited) differentiator as
$$ H_d(e^j omega) = j fracomegaT ~~~, ~~~~ |omega| < pi$$
The associated discrete-time (IIR) impulse response is
$$ h_d[n] = textIDTFT H_d(e^j omega) = textIDTFT j fracomegaT $$
Practically you would truncate and window $h_d[n]$ before using.
So they are not the same.
answered Nov 13 '18 at 23:08
Fat32Fat32
14.7k31229
edited Nov 13 '18 at 23:18
answered Nov 13 '18 at 23:08
Fat32Fat32
14.7k31229
answered Nov 13 '18 at 23:08
Fat32Fat32
14.7k31229
answered Nov 13 '18 at 23:08
Fat32Fat32
14.7k31229
14.7k31229
add a comment |
add a comment |
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$begingroup$
1) Aliasing. The ideal derivative filter you're using in the frequency domain is infinitely long in the time domain. You're getting aliases in the time domain. 2) [1, -1] as filter taps is a scaled truncation of the ideal derivative filter. It is in error at high frequencies. So in light of 1 & 2, you should really specify over which frequencies how much error you can tolerate. Then you can build a filter to that specification.
$endgroup$
– Andy Walls
Nov 13 '18 at 22:51
$begingroup$
BTW, GNURadio has a little stand alone utility for building Minimum Mean Squared Error differentiating FIR filter taps. GNURadio uses it to build a polyphase filter bank for an interpolating differentiator, with 8 taps in each polyphase filter arm, and MMSE over the frequencies in the interval $[-f_s/4, f_s/4]$. github.com/gnuradio/gnuradio/tree/master/gr-filter/lib/…
$endgroup$
– Andy Walls
Nov 13 '18 at 23:08