Image Homomorphic Filtering Using Fourier Transform

In this article, we explore the implementation of image homomorphic filtering using Fourier transform to solve uneven illumination issues that affect image quality. We provide a comprehensive explanation of Fourier transform concepts and their application in image processing workflows. The discussion covers the fundamental principles of homomorphic filtering, including how it separates illumination and reflectance components in the frequency domain. We demonstrate practical implementation approaches using key functions such as FFT (Fast Fourier Transform) for frequency domain conversion and logarithmic operations for component separation. The article includes algorithmic explanations of filter design in the frequency domain, focusing on Butterworth or Gaussian high-pass filters to enhance reflectance components while suppressing low-frequency illumination variations. Finally, we present real-world case studies showing how homomorphic filtering effectively resolves illumination problems through inverse Fourier transformation and exponential operations to reconstruct enhanced images. This comprehensive guide will help you understand core image processing principles and their practical applications in computer vision systems.