Fast Fourier Transform for Image Processing with High-Frequency Removal

MATLAB 430K 350 views 0 downloads 1 credits
Tags:

Resource Overview

Performing Fast Fourier Transform on images, removing high-frequency information in the frequency domain, and applying inverse transform to spatial domain to eliminate image raster artifacts with implementation insights.

Detailed Documentation

Applying Fast Fourier Transform (FFT) to images enables analysis and processing in the frequency domain. By removing high-frequency components through filtering techniques (e.g., using low-pass filters or frequency masking) and subsequently performing inverse FFT, this method effectively eliminates raster artifacts from images. Implementation typically involves converting the image to grayscale, applying 2D FFT (via functions like fft2 in MATLAB or numpy.fft.fft2 in Python), creating a frequency mask to attenuate high frequencies centered around the DC component, and applying inverse FFT to restore the spatial domain image. This process results in enhanced image clarity and smoothness by suppressing noise and periodic patterns while preserving essential low-frequency structural information.

You May Also Like