Bahadir K. Gunturk2
Frequency-Domain Filtering
Compute the Fourier Transform of the image
Multiply the result by filter transfer function
Take the inverse transform
Bahadir K. Gunturk4
Frequency-Domain Filtering
Ideal Lowpass Filters
1, for and
( , )
0, otherwise
u v
u D v D
H u v
>> [f1,f2] = freqspace(256,'meshgrid');
>> H = zeros(256,256); d = sqrt(f1.^2 + f2.^2) < 0.5;
>> H(d) = 1;
>> figure; imshow(H);
Separable
Non-separable
>> [f1,f2] = freqspace(256,'meshgrid');
>> H = zeros(256,256); d = abs(f1)<0.5 & abs(f2)<0.5;
>> H(d) = 1;
>> figure; imshow(H);
2 2
0
1, for
( , )
0, otherwise
u v D
H u v
5.
Bahadir K. Gunturk5
Frequency-Domain Filtering
Butterworth Lowpass Filter
2
2 2
0
1
( , )
1
n
H u v
u v D
As order increases the
frequency response
approaches ideal LPF
6.
Bahadir K. Gunturk6
Frequency-Domain Filtering
Butterworth Lowpass Filter
Approach to a sinc function.
7.
Bahadir K. Gunturk7
Frequency-Domain Filtering
Gaussian Lowpass Filter
2 2
0
( , )
u v
D
H u v e
Bahadir K. Gunturk10
Highpass Filters
2
2 2
0
1
( , )
1
n
H u v
u v D
2 2
0
( , ) 1
u v
D
H u v e
2 2
0
0, for
( , )
1, otherwise
u v D
H u v
Bahadir K. Gunturk12
Homomorphic Filtering
Consider the illumination and reflectance components of
an image ( , ) ( , )* ( , )
f x y i x y r x y
Illumination Reflectance
ln ( , ) ln ( , ) ln ( , )
f x y i x y r x y
Take the ln of the image
In the frequency domain
( , ) ( , ) ( , )
i r
F u v F u v F u v
13.
Bahadir K. Gunturk13
Homomorphic Filtering
The illumination component of an image shows slow
spatial variations.
The reflectance component varies abruptly.
Therefore, we can treat these components somewhat
separately in the frequency domain.
1
With this filter, low-frequency components are attenuated, high-frequency
components are emphasized.