Відео

Image negatives can make certain details more visible, especially in images where the background is dark and the foreground is light. In medical imaging, such as X-rays, image negatives can help highlight specific features that might be less visible in the original image.

​@@StudywithDrDafdathank you so much sir..

Thank you.

In chapter 2 Image enhancements and filtering, both spatial domain and frequency domain filtering ppts are present.

No. ND(P) are four diagonal neighbours, while N8(P) is all eight neighbours of P. That is N8(P)=N4(P)+ND(P). While considering neighbourhood pixels.

Yes, you can say that, while considering adjacency.

The set V is constant for range {0 to 10}, which means V can range only from 0 to 10. And we have to consider only values in between 0 to 10.

Code is present in the description box.

It is a inbuilt MATLAB function.

At 7:28, the 7 output value has FD in the dictionary, and not FDA. Check dictionary location number 7, it is FD and therefore always last character is required for correct decoding.

Thank you.

ua-cam.com/video/uTdBFr8Fn-w/v-deo.html

You are most welcome

You can try to remember the concepts.

Replace that line with I=I+100; This should solve your error.

@@StudywithDrDafda still error sir imadd requires Image Processing Toolbox. Error in untitled (line 6) I = imadd(I=I+100);

Yes, it will require Image Processing Toolbox for execution. You need to install it .

Done sorting. Thanks for the suggestion.

👍

% Matlab program for Butterworth High Pass Filter(BHPF) clc; clear; close all; % Read the image %a = imread('Maulik.png'); %a = rgb2gray(a); a = imread('Cameraman.tif'); a = im2double(a); %convert the range of colors from 0-255 to 0-1 [m,n] =size(a); %Get size of image subplot(2,3,1) imshow(a) title('Input image'); A = fft2(a); %fourier transform of 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); %Magnitude of A_shift(Frequency domain representation of image) subplot(2,3,3) imshow(uint8(A_shift)); title('Frequency domain image'); D0 = 30; %Cut-Off frequency D = zeros(m,n); order=20;% order of butterworth filter % Butterworth High pass filtering for i=1:m for j=1:n D(i,j) = sqrt(((i-(m/2))^2+(j-(n/2))^2)); H(i,j)=1/((1+(D0/D(i,j))^(2*order))); end end butter_HPF = A_shift.*H; butter_HPF_inverse = ifft2(butter_HPF); butter_HPF_real = abs(butter_HPF_inverse); subplot(2,3,4) imshow(H) title('Butterworth HP Filter'); subplot(2,3,5); mesh(H) title('surface plot BHPF') subplot(2,3,6) imshow((butter_HPF_real)); title('Butterworth HP Filtered image');

% Matlab program for Gaussian High Pass Filter(GHPF) clc; clear; close all; % Read the image a = imread('Maulik.png'); a = rgb2gray(a); %a=imread('Cameraman.tif'); a = im2double(a); %convert the range of colors from 0-255 to 0-1 [m,n] =size(a); %Get size of image subplot(2,3,1) imshow(a) title('Input image'); A = fft2(a); %fourier transform of 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); %Magnitude of A_shift(Freq. domain repre.) subplot(2,3,3) imshow(uint8(A_real)); title('Frequency domain image'); D0 = 30; %Cut-Off frequency OR Standard deviation sigma for u=1:m for v=1:n D = sqrt((u-m/2).^2+(v-n/2).^2); H(u,v) = 1 - exp(-(D^2)/(2*D0.^2)); end end H_high = H.*A_shift; H_high_real = H.*A_real; H_high_shift = fftshift(H_high); H_high_image = ifft2(H_high_shift); subplot(2,3,4) imshow(H) title('Gaussian High pass Filter'); subplot(2,3,5); mesh(H) title('surface plot GHPF') subplot(2,3,6) imshow(abs(H_high_image)); title('Gaussian High pass Filtered image');

clc; clear; close all; % Read the image %a = imread('Maulik.png'); %a = rgb2gray(a); a=imread('Cameraman.tif'); a = im2double(a); %convert the range of colors from 0-255 to 0-1 [m,n] =size(a); %Get size of image subplot(2,3,1) imshow(a) title('Input image'); A = fft2(a); %fourier transform of 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); %Magnitude of A_shift(Frequency domain representation of image) subplot(2,3,3) imshow(uint8(A_real)); title('Frequency domain image'); D0 = 50; %Cut-Off frequency D = zeros(m,n); order=2;% order of butterworth filter % Butterworth Low pass filtering for u=1:m for v=1:n D(u,v) = sqrt(((u-(m/2))^2+(v-(n/2))^2)); H(u,v) = 1/((1+(D(u,v)/D0)^(2*order))); end end subplot(2,3,4) imshow(H) title('Butterworth LP Filter'); subplot(2,3,5); mesh(H) title('surface plot BLPF') butter_LPF = A_shift.*H; butter_LPF_inverse = ifft2(butter_LPF); butter_LPF_real = abs(butter_LPF_inverse); subplot(2,3,6) imshow((butter_LPF_real)); title('Butterworth LP Filtered image');

clc; clear; close all; % Read the image %a = imread('Maulik.png'); %a = rgb2gray(a); a=imread('Cameraman.tif'); a = im2double(a); %convert the range of colors from 0-255 to 0-1 [m,n] =size(a); %Get size of image subplot(2,3,1) imshow(a) title('Input image'); A = fft2(a); %fourier transform of 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); %Magnitude of A_shift(Freq. domain repre.) subplot(2,3,3) imshow(uint8(A_real)); title('Frequency domain image'); D0 = 50; %Cut-Off frequency OR Standard deviation sigma % Gaussian Low pass filtering for u=1:m for v=1:n d = sqrt((u-m/2).^2+(v-n/2).^2); H(u,v)=exp(-(d^2)/(2*D0.^2)); end end H_low = H.*A_shift; H_low_real = H.*A_real; H_low_shift = fftshift(H_low); H_low_image = ifft2(H_low_shift); subplot(2,3,4) imshow(H) title('Gaussian Low pass Filter'); subplot(2,3,5); mesh(H) title('surface plot GLPF') subplot(2,3,6) imshow(abs(H_low_image)); title('Gaussian Low pass Filtered image');

Plz clarify my dout

We are working on term paper ieee lossless compression of jpeg coded photo collection did you have any idea on it

The calculations are done as per the steps explained in the video.

The MATLAB calculations are done as per the steps discussed in the video.

@StudywithDrDafda these formulas i.e forward and inverse are used to find quantization table ?

Yes, correct.

No. For Huffman coding, decoded image is same as input image. Coded image is compressed image.

@@StudywithDrDafda yes tanq

@StudywithDrDafda sir for jpeg compression Huffman coding and arithmetic encoding or else any existing methods can you say plz

All topics covered in the playlist: Digital Image Processing: ua-cam.com/play/PL0rq1NWOtSWdMxKHShC5iBitElZaaJOpw.html

For saving the image you can use imwrite().

Thank you.

Image quality measures are represented in decibels or on a logarithmic scale because human perception of image quality is nonlinear and very wide. Logarithmic scale accounts for varying sensitivity across intensity levels and offers a compact representation for wide dynamic ranges. It simplifies comparison between images and systems, facilitates standardization, and enables mathematical modeling. In addition, it helps in compressing pixel intensity values ​​and provides insight into system behavior.

ua-cam.com/video/Sq-2quHpkE0/v-deo.html

Homomorphic filters are often applied to grayscale images in image processing because they are designed to enhance the contrast in an image by separating its illumination and reflectance components. When applied to color images, the filtering process may disrupt the natural color balance, leading to unintended color artifacts or distortions. Additionally, the complexity of handling multiple color channels makes the application of homomorphic filters on color images more challenging and less straightforward.