Image Contrast Enhancement and Dehazing Using Homomorphic Filtering

In digital image processing workflows, homomorphic filtering serves as a powerful technique for simultaneously enhancing image contrast and removing atmospheric haze effects from color images. This frequency-domain approach operates by separating illumination and reflectance components through logarithmic transformation and Fourier analysis. The core algorithm typically involves: applying natural logarithm to pixel values, performing Fast Fourier Transform (FFT), designing a butterworth high-pass filter in frequency domain, multiplying the filter with transformed image spectrum, applying inverse FFT, and finally exponentiating the results to restore enhanced pixel values. Key implementation considerations include proper filter parameter tuning (cutoff frequency and order selection) and handling color channels through individual processing or HSV color space conversion to preserve color fidelity. This method effectively amplifies high-frequency details while suppressing low-frequency haze components, making it particularly suitable for enhancing underwater imagery, aerial photography, and fog-affected color images with balanced color preservation.