HSV-Based Shadow Detection and Removal

This code primarily focuses on HSV-based shadow detection and removal. The implementation begins with color space conversion, transforming the input image from RGB to HSV format. The HSV color space closely mimics human color perception, enabling more precise shadow recognition. The algorithm then separately extracts foreground and background images to facilitate targeted shadow processing. By applying specific HSV thresholding techniques—typically focusing on Value (V) and Saturation (S) components—the method effectively identifies and eliminates shadows from images, thereby enhancing overall image quality. Additionally, the computational simplicity of HSV operations allows for efficient shadow detection and removal processing. The core implementation involves functions for color space conversion (e.g., cv2.cvtColor() in OpenCV), component thresholding, and morphological operations for shadow refinement. This HSV-based approach provides a straightforward yet effective solution for improving visual quality in image processing applications.