Performing FFT Calculation on Read Images with Code Implementation

This process involves performing Fast Fourier Transform (FFT) on loaded images, then separately extracting magnitude and phase information for image restoration, followed by comparison of reconstruction results. The implementation typically uses fft2() function in MATLAB or np.fft.fft2() in Python for 2D FFT computation, which transforms images from spatial domain to frequency domain for enhanced spectral analysis. By analyzing magnitude spectra (representing intensity information) and phase spectra (containing structural details), we can reconstruct original image features using ifft2() inverse transform operations. This frequency-domain approach reveals hidden image characteristics through magnitude-phase decomposition, improving precision in image processing applications like filtering and compression. The restoration comparison typically involves calculating Mean Squared Error (MSE) or Structural Similarity Index (SSIM) between original and reconstructed images to validate processing effectiveness.