Performing Fourier Transform on Images with Code Implementation

Before applying Fourier transform to image processing, preliminary preprocessing is essential. Preprocessing steps typically involve image grayscale conversion using functions like rgb2gray(), noise reduction through filters such as median or Gaussian filters, and edge detection using operators like Sobel or Canny. Following preprocessing, we implement Fourier Transform (commonly using fft2() in MATLAB) to analyze the image's frequency spectrum. This transformation converts spatial domain images into frequency domain representations, allowing visualization of various frequency components through spectrum analysis. After Fourier transformation, specific graphical processing techniques are employed to eliminate horizontal stripes. This involves applying frequency domain filters like band-stop or notch filters to target stripe-related frequencies in the Fourier spectrum. Image inpainting algorithms (e.g., using MATLAB's regionfill() function) can also reconstruct corrupted areas while preserving image details. Finally, Inverse Fourier Transform (implemented via ifft2()) is applied to convert the processed frequency data back to spatial domain, generating the final output image. This pipeline effectively enhances image quality by combining Fourier analysis with targeted frequency manipulation techniques.