Retinex Algorithm Implementation with PCA Chromaticity Decomposition and Enhanced Bilateral Filtering

This documentation presents a Retinex algorithm implementation that incorporates Principal Component Analysis (PCA) for chromatic decomposition and an enhanced bilateral filtering technique for low-frequency and high-frequency component separation. The algorithm begins by applying PCA to decompose image chromatic information, which effectively handles color correlation and enhances contrast by redistributing color components along principal axes. The implementation then utilizes a modified bilateral filter that improves upon traditional approaches by adaptively adjusting spatial and range parameters based on local image characteristics. This enhanced filtering mechanism accurately isolates low-frequency components (representing illumination) from high-frequency details (representing reflectance), crucial for Retinex-based image enhancement. Through this dual decomposition approach, the algorithm enables superior analysis of image details and features, with PCA chromatic decomposition particularly effective at boosting contrast to emphasize important visual elements. The code structure includes modular functions for PCA transformation, bilateral filtering with parameter optimization, and frequency component recombination, making it adaptable for various image processing scenarios. Overall, this implementation expands image processing possibilities by providing robust tools for handling diverse image types through sophisticated decomposition and enhancement techniques.