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**Digital Image Processing**

بسمهتعالي Digital Image Processing Image Enhancement in the Frequency Domain (Chapter 4) H.R. Pourreza H.R. Pourreza

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Objective Basic understanding of the frequency domain, Fourier series, and Fourier transform. Image enhancement in the frequency domain. H.R. Pourreza

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**Fourier Series Any function that periodically**

repeats itself can be expressed as the sum of sines and/or cosines of different frequencies, each multiplied by a different coefficients. This sum is called a Fourier series. H.R. Pourreza

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Fourier Series H.R. Pourreza

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Fourier Transform A function that is not periodic but the area under its curve is finite can be expressed as the integral of sines and/or cosines multiplied by a weighing function. The formulation in this case is Fourier transform. H.R. Pourreza

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**Continuous One-Dimensional Fourier Transform and Its Inverse**

Where (u) is the frequency variable. F(u) is composed of an infinite sum of sine and cosine terms and… Each value of u determines the frequency of its corresponding sine-cosine pair. H.R. Pourreza

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**Continuous One-Dimensional Fourier Transform and Its Inverse**

Example Find the Fourier transform of a gate function (t) defined by 2 4 u 0.5 - 0.5 x H.R. Pourreza

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**Continuous One-Dimensional Fourier Transform and Its Inverse**

H.R. Pourreza

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**Discrete One-Dimensional Fourier Transform and Its Inverse**

A continuous function f(x) is discretized into a sequence: by taking N or M samples x units apart. H.R. Pourreza

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**Discrete One-Dimensional Fourier Transform and Its Inverse**

Where x assumes the discrete values (0,1,2,3,…,M-1) then The sequence {f(0),f(1),f(2),…f(M-1)} denotes any M uniformly spaced samples from a corresponding continuous function. H.R. Pourreza

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**Discrete One-Dimensional Fourier Transform and Its Inverse**

u =[0,1,2, …, M-1] x =[0,1,2, …, M-1] H.R. Pourreza

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**Discrete One-Dimensional Fourier Transform and Its Inverse**

The values u = 0, 1, 2, …, M-1 correspond to samples of the continuous transform at values 0, u, 2u, …, (M-1)u. i.e. F(u) represents F(uu), where: Each term of the FT (F(u) for every u) is composed of the sum of all values of f(x) H.R. Pourreza

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**Discrete One-Dimensional Fourier Transform and Its Inverse**

The Fourier transform of a real function is generally complex and we use polar coordinates: H.R. Pourreza

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**Discrete One-Dimensional Fourier Transform and Its Inverse**

|F(u)| (magnitude function) is the Fourier spectrum of f(x) and (u) its phase angle. The square of the spectrum is referred to as the Power Spectrum of f(x) (spectral density). H.R. Pourreza

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**Discrete 2-Dimensional Fourier Transform**

Fourier spectrum: Phase: Power spectrum: H.R. Pourreza

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**Discrete One-Dimensional Fourier Transform and Its Inverse**

H.R. Pourreza

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**Time and Frequency Resolution and Sampling**

Fmax = 100 Hz What is the sampling rate (Nyquist)? What is the time resolution? What is the frequency resolution? What if the sampling rate is higher than the Nyquist sampling rate? What if we take samples for two seconds with the Nyquist sampling rate? 1 second H.R. Pourreza

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**Discrete Two-Dimensional Fourier Transform and Its Inverse**

Fourier Spectrum H.R. Pourreza

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**Discrete Two-Dimensional Fourier Transform and Its Inverse**

F(0,0) is the average intensity of an image H.R. Pourreza

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**Discrete Two-Dimensional Fourier Transform and Its Inverse**

Use Matlab to generate the above figures H.R. Pourreza

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**Frequency Shifting Property of the Fourier Transform**

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**Frequency Shifting Property of the Fourier Transform**

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**Basic Filtering in the Frequency Domain using Matlab**

function Normalized_DFT = Img_DFT(img) img=double(img); % So mathematical operations can be conducted on % the image pixels. [R,C]=size(img); for r = 1:R % To phase shift the image so the DFT will be for c=1:C % centered on the display monitor phased_img(r,c)=(img(r,c))*(-1)^((r-1)+(c-1)); end fourier_img = fft2(phased_img); %Discrete Fourier Transform mag_fourier_img = abs(fourier_img ); % Magnitude of DFT Log_mag_fourier_img = log10(mag_fourier_img +1); Max = max(max(Log_mag_fourier_img )); Normalized_DFT = (Log_ mag_fourier_img )*(255/Max); imshow(uint8(Normalized_DFT)) H.R. Pourreza

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**Basic Filtering in the Frequency Domain**

Multiply the input image by (-1)x+y to center the transform Compute F(u,v), the DFT of the image from (1) Multiply F(u,v) by a filter function H(u,v) Compute the inverse DFT of the result in (3) Obtain the real part of the result in (4) Multiply the result in (5) by (-1)x+y H.R. Pourreza

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**Filtering out the DC Frequency Component**

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**Filtering out the DC Frequency Component**

Notch Filter otherwise H.R. Pourreza

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**Low-pass and High-pass Filters**

Low Pass Filter attenuate high frequencies while “passing” low frequencies. High Pass Filter attenuate low frequencies while “passing” high frequencies. H.R. Pourreza

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**Low-pass and High-pass Filters**

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**Low-pass and High-pass Filters**

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**Low-pass and High-pass Filters**

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**Smoothing Frequency Domain, Ideal Low-pass Filters**

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**Smoothing Frequency Domain, Ideal Low-pass Filters**

Total Power The remained percentage power after filtration H.R. Pourreza

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**Smoothing Frequency Domain, Ideal Low-pass Filters**

fc =5 = 92% fc =30 = 96.4% fc =15 = 94.6% fc =80 = 98% fc =230 = 99.5% H.R. Pourreza

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Cause of Ringing H.R. Pourreza

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**Project #5, Ideal Low-pass Filter**

Implement in Matlab the Ideal low-pass filter in the following equation. You must give the user the ability to specify the cutoff frequency D0 Calculate the The remained percentage power after filtration. Display the image after filtration Use Elaine image to test your program H.R. Pourreza

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**Smoothing Frequency Domain, Butterworth Low-pass Filters**

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**Smoothing Frequency Domain, Butterworth Low-pass Filters**

Butterworth Low-pass Filter: n=2 Radii= 5 Radii= 15 Radii= 30 Radii= 80 Radii= 230 H.R. Pourreza

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**Smoothing Frequency Domain, Butterworth Low-pass Filters**

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**Smoothing Frequency Domain, Gaussian Low-pass Filters**

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**Smoothing Frequency Domain, Gaussian Low-pass Filters**

Radii= 5 Radii= 15 Radii= 30 Radii= 80 Radii= 230 H.R. Pourreza

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**Smoothing Frequency Domain, Gaussian Low-pass Filters**

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**Smoothing Frequency Domain, Gaussian Low-pass Filters**

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**Smoothing Frequency Domain, Gaussian Low-pass Filters**

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**Sharpening Frequency Domain Filters**

Hhp(u,v) = 1 - Hlp(u,v) H.R. Pourreza

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**Sharpening Frequency Domain Filters**

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**Sharpening Frequency Domain, Ideal High-pass Filters**

H.R. Pourreza

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**Sharpening Frequency Domain, Butterworth High-pass Filters**

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**Sharpening Frequency Domain, Gaussian High-pass Filters**

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**Homomorphic Filtering**

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**Homomorphic Filtering**

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**Convolution h(t) g(t) k(t) g(t) = k(t)*h(t) G(f) = K(f)H(f)**

* is a convolution operator and not multiplication 1 2 3 t k(t) -1 1 t h(t) H.R. Pourreza

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Convolution -1 1 m h(m) g(t) m k(-m) H.R. Pourreza

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Convolution -1 1 m h(m) g(t) m k(1-m) H.R. Pourreza

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Convolution -1 1 m h(m) g(t) m k(2-m) H.R. Pourreza

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Convolution -1 1 m h(m) g(t) m k(3-m) H.R. Pourreza

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Convolution -1 1 m h(m) g(t) m k(4-m) H.R. Pourreza

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Convolution -1 1 m h(m) g(t) m k(5-m) H.R. Pourreza

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Convolution -1 1 m h(m) g(t) m k(6-m) H.R. Pourreza

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**2-Dimensions Convolution**

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**Correlation g(t) = k(t)h(t) G(f) = K(f)* . H(f) h(t) k(t) g(t) -1 1 2**

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Correlation H.R. Pourreza

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Convolution H.R. Pourreza

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Convolution H.R. Pourreza

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Convolution H.R. Pourreza

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Convolution H.R. Pourreza

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