Image Processing with Fourier Transform

1. First, apply Fourier Transform to the image, a mathematical technique that converts the image from spatial domain to frequency domain for better analysis and processing. In implementation, this can be achieved using functions like fft2() in MATLAB or np.fft.fft2() in Python, which compute the 2D Fast Fourier Transform of the image matrix.

2. Next, based on the Fourier Transform results, vertical stripe noise can be removed from the image to enhance quality and clarity. This involves identifying and filtering out frequency components corresponding to the noise pattern using techniques like frequency domain masking or notch filtering algorithms in the Fourier spectrum.

3. Finally, apply Inverse Fourier Transform to convert the frequency domain representation back to spatial domain, obtaining the segmented/processed image. This is typically implemented using ifft2() in MATLAB or np.fft.ifft2() in Python, which reconstructs the image from the modified frequency components while preserving the noise-free characteristics.