can anyone explain me dis gaussian low pass filter coding

[M N]=size(A); R=10; X=0:N-1; Y=0:M-1; [X Y]=meshgrid(X,Y); Cx=0.5*N; Cy=0.5*M; Lo=exp(-((X-Cx).^2+(Y-Cy).^2)./(2*R).^2); Hi=1-Lo; J=A1.*Lo; J1=ifftshift(J); B1=ifft2(J1); K=A1.*Hi; K1=ifftshift(K); B2=ifft2(K1);

Answer 1

[Hari]

Hi Shri Ram,

I understand that you want an explanation of the code implementing a Gaussian low-pass filter on an image. This code uses frequency domain filtering techniques.

Here are the details:

Initialization and Parameters:

The code determines the size of the input image "A" using "[M, N] = size(A)", where "M" is the number of rows and "N" is the number of columns.

"R" is the cutoff radius for the Gaussian filter, which determines how much of the high-frequency content is attenuated.

Meshgrid Creation:

The vectors "X" and "Y" are created to represent the frequency domain grid using "meshgrid". "X" and "Y" span from 0 to "N-1" and 0 to "M-1", respectively.

Centering the Gaussian Filter:

"Cx" and "Cy" are the center coordinates of the frequency domain, calculated as half of "N" and "M", respectively.

The Gaussian low-pass filter "Lo" is created using the formula "exp(-((X-Cx).^2+(Y-Cy).^2)./(2*R).^2)". This filter attenuates high-frequency components, allowing low-frequency components to pass through.

High-pass Filter Creation:

The high-pass filter "Hi" is computed as "1 - Lo". This filter allows high-frequency components to pass while attenuating low-frequency components.

Applying the Low-pass Filter:

The input image in the frequency domain "A1" is multiplied by the low-pass filter "Lo" to obtain "J".

"ifftshift" is applied to "J" to shift the zero-frequency component back to the origin, resulting in "J1".

The inverse Fourier transform "ifft2" is applied to "J1" to obtain the filtered image "B1" in the spatial domain.

Applying the High-pass Filter (Optional):

Similarly, the input image in the frequency domain "A1" is multiplied by the high-pass filter "Hi" to obtain "K".

"ifftshift" is applied to "K" to shift the zero-frequency component back to the origin, resulting in "K1".

The inverse Fourier transform "ifft2" is applied to "K1" to obtain the high-pass filtered image "B2" in the spatial domain.

Refer to the documentation of MATLAB's "meshgrid" function for more details on creating coordinate matrices: https://www.mathworks.com/help/matlab/ref/meshgrid.html

Refer to the documentation of MATLAB's "ifft2" function for more details on the inverse 2D Fourier transform: https://www.mathworks.com/help/matlab/ref/ifft2.html

Refer to the documentation of MATLAB's "ifftshift" function for more details on shifting zero-frequency components: https://www.mathworks.com/help/matlab/ref/ifftshift.html

Hope this helps!