Homomorphic Filtering in Digital Image Processing and its Implementation in MATLAB||#DIP

Video lecture series on Digital Image Processing, Lecture: 22, Homomorphic Filtering in Digital Image Processing and its Implementation in MATLAB What is Homomorphic filter? Which are different steps in Homomorphic filtering? Classification of frequency domain filtering Which approach is used for Homomorphic filtering? Where homomorphic filtering is used? How to derive equation for Homomorphic filter? What are the advantages of Homomorphic filtering? MATLAB code used in the video is present at the end in Description Digital Image Processing (DIP) using/in MATLAB Link to download ppts/lecture notes: https://drive.google.com/drive/folders/1AtR1eq6ZvQf-5vjXEMUPXNj2S0d9rAMZ?usp=share_link #DIP #DIPwithMATLAB #DigitalImageProcessingUsingMATLAB #DigitalImageProcessing #StudywithDrDafda Links of other lectures in the series: 1. What is Digital Image Processing? https://youtu.be/RkcX28FnnO0 2. Human Visual System and Elements of Digital Image Processing https://youtu.be/DSpDs4si3Os 3. Fundamental steps in Digital Image Processing https://youtu.be/UUZUzoBkkls 4. Image Sensing and Acquisition https://youtu.be/kbzb_J9O7Jg 5. Relationship between Pixels in Digital Image Processing: Neighborhood, Adjacency & Distance measures https://youtu.be/bEaQnKr3LAc 6. Image Sampling and Quantization https://youtu.be/yKj-xGepD8I 7. Spatial and Intensity resolution in Digital Image Processing and its Implementation in MATLAB https://youtu.be/5HJgnZmDJi0 8. Basics of intensity transformations and spatial filtering and implementation in MATLAB https://youtu.be/8_TGc7-iTyg 9. Image negatives, Log and Power-Law transformations for DIP and implementation in MATLAB https://youtu.be/RepCjy1D7lU 10. Piecewise linear transformation function: Contrast Stretching in DIP & implementation in MATLAB https://youtu.be/eH_Ht-O0ufg 11. Piecewise linear transformation function: Intensity-level slicing in DIP and implementation in MATLAB https://youtu.be/4WqGAUpocnk 12. Piecewise linear transformation function: Bit-plane slicing in DIP and implementation in MATLAB https://youtu.be/rnWDeG0RFDY 13. Histogram Equalization in DIP and its implementation in MATLAB https://youtu.be/aAXnY0fAFGE 14. Histogram Matching/Specification in Digital Image Processing with example and perform in MATLAB https://youtu.be/u-KwOsRCrrg 15. Fundamentals of Spatial filtering and Smoothing spatial filters in Digital Image Processing & MATLAB https://youtu.be/OTl2wqc6ng8 16. Order statistics/Non-linear (Median, Minimum and Maximum) spatial filters in DIP with example & Implementation in MATLAB https://youtu.be/gu0DDrw7kS8 17. Image Sharpening in Digital Image Processing||Sharpening Spatial filters with examples||HPF||MATLAB https://youtu.be/OQEKAqqhn1g 18. Introduction to Image Enhancement in the frequency domain and different steps for filtering in the frequency domain for DIP https://youtu.be/n7FNrXTO_4A 19. Image Smoothing in frequency domain filtering and its Implementation in MATLAB https://youtu.be/fulN0Tkw0lM 20. Image Sharpening (HPF) in frequency domain filtering and its Implementation in MATLAB https://youtu.be/9ycskBCJC4I 21. Laplacian, Unsharp masking/High Boost filtering in the frequency domain filtering and its Implementation in MATLAB https://youtu.be/GimpUJkQvCQ % MATLAB program for Homomorphic filtering clc; clear all; close all; % Read the image a = imread('Maulik.png'); a = rgb2gray(a); %a = imread('AT3_1m4_01.tif'); %a= imread('trees.tif'); %a = imread('cameraman.tif'); % Input image subplot(2,3,1); imshow(a); title('Input Image'); a = double(a); b = a; %constants required for the filter D0 = 50; GL = 0.9; GH = 1.9; [m,n] = size(a); % size of input image b = b+1;% add 1 to pixels to remove 0 values which %would result in undefined log values log_b = log(b); % Taking log subplot(2,3,2); imshow(log_b); title('Natural Logarithm'); c = fft2(log_b); % Taking the fft subplot(2,3,3); imshow(uint8(c)); title('Fourier transform'); dd = fftshift(c); for u=1:m for v=1:n H(u,v) = (GH - GL)*(1-exp(-1*(sqrt((u-m/2)^2+(v-n/2)^2))^2/D0)^2)+GL; end end subplot(2,3,4); mesh(H); title('Homomorphic filter'); x = dd.*H; real_x = abs(ifft2(x)); subplot(2,3,5); imshow(real_x); title('Inverse fourier transform'); Final = exp(real_x); subplot(2,3,6); imshow(uint8(Final)); title('Filtered Image');