Image Smoothing(LPF) in frequency domain filtering & its Implementation in MATLAB| ILPF |BLPF |GLPF

Video lecture series on Digital Image Processing, Lecture: 19,
Image Smoothing in frequency domain filtering and its Implementation in MATLAB, ILPF: Ideal Low-Pass filter, BLPF: Butterworth Low-Pass Filter and GLPF: Gaussian Low-Pass filters explanation with programs in MATLAB
Classification of frequency domain Low Pass filtering
What are differences between ILPF, BLPF and GLPF
What is ringing effect? How to solve the problem of ringing effect?
Which frequency domain low pass filter is most advantagesous and why?
Digital Image Processing (DIP) using/in MATLAB

Link to download ppts/lecture notes:
https://drive.google.com/drive/folder...

MATLAB code used in the video is present at the end in the Description
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Links of other lectures in the series:
1. What is Digital Image Processing?
What is Digital Image Processing || I...

2. Human Visual System and Elements of Digital Image Processing
Human Visual System and  Elements of ...

3. Fundamental steps in Digital Image Processing
Fundamental steps in Digital Image Pr...

4. Image Sensing and Acquisition
Image Sensing and Acquisition in Digi...

5. Relationship between Pixels in Digital Image Processing: Neighborhood, Adjacency & Distance measures
Relationship between Pixels in Digita...

6. Image Sampling and Quantization
Image Sampling and Quantization in Di...

7. Spatial and Intensity resolution in Digital Image Processing and its Implementation in MATLAB
Spatial and Intensity Resolution in D...

8. Basics of intensity transformations and spatial filtering and implementation in MATLAB
Basics of Intensity transformations a...

9. Image negatives, Log and Power-Law transformations for DIP and  implementation in MATLAB
Intensity (Image negatives, Log and P...

10. Piecewise linear transformation function: Contrast Stretching in DIP & implementation in MATLAB
Piecewise linear transformation funct...

11. Piecewise linear transformation function: Intensity-level slicing in DIP and  implementation in MATLAB
Piecewise linear transformation funct...

12. Piecewise linear transformation function: Bit-plane slicing in DIP and  implementation in MATLAB
Piecewise linear transformation funct...

13. Histogram Equalization in DIP and its implementation in MATLAB
Histogram equalization/processing in ...

14. Histogram Matching/Specification in Digital Image Processing with example and perform in MATLAB
Histogram Matching/Specification in D...

15. Fundamentals of Spatial filtering and Smoothing spatial filters in Digital Image Processing & MATLAB
Fundamentals of Spatial filtering and...

16. Order statistics/Non-linear (Median, Minimum and Maximum) spatial filters  in DIP with example & Implementation in MATLAB  
Order statistics/Non-linear(Median, M...

17. Image Sharpening in Digital Image Processing||Sharpening Spatial filters with examples||HPF||MATLAB
Image Sharpening in Digital Image Pro...

18. Introduction to Image Enhancement in the frequency domain and different steps for filtering in the frequency domain for DIP
Intro. to Image Enhancement in the fr...

%Matlab program for Ideal low pass filter in the frequency domain
clc;
clear;
close all;
%a = imread('Maulik.png');
%a = rgb2gray(a);
a = imread('Cameraman.tif'); % Input image
a = im2double(a);
subplot(2,3,1);
imshow(a);
title('Input Image');

[m,n] = size(a);  % size of input image
D0 = 50; % Assigning Cut-off Frequency  
A = fft2(a);   %fourier transform of input image
subplot(2,3,2);
imshow(uint8(abs(A)));
title('F.T. of i/p without shift');

A_shift = fftshift(A);  %shifting origin
A_real = abs(A_shift);  %Real part of A_shift (Freq domain repres of image)

subplot(2,3,3);
imshow(uint8(A_real));
title('F.T. of i/p after shift');

A_low = zeros(m,n);
d = zeros(m,n);
for u=1:m
   for v=1:n
       d(u,v)=sqrt((u-(m/2))^2+(v-(n/2))^2);
       if d(u,v){is less than or equal to}D0
           A_low(u,v)=A_shift(u,v);
           filt(u,v) = 1;
       else
           A_low(u,v)=0;
           filt(u,v) = 0;
       end
   end
end

subplot(2,3,4);
imshow(filt)
title('Ideal Low pass filter')

subplot(2,3,5);
mesh(filt)
title('surface plot LPF')
B = fftshift(A_low);  %Reshifting the origin of filtered image
B_inverse = ifft2(B); %Taking inverse fourier transform
B_real = abs(B_inverse);%Taking real part(Low pass output image)

subplot(2,3,6);
imshow(B_real);
title('Low pass image');

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