Enhancing Visual Clarity via Color Model-Based Histogram Equalization

This paper proposes a novel approach for enhancing hazy images using histogram methods and color space conversions. The method involves converting the hazy image into RGB, HSV, and YCbCr color spaces, where each space offers unique insights into the image’s color and brightness characteristics. By applying histogram equalization and other histogram-based techniques in these color spaces, the hazy image is effectively dehazed, resulting in improved contrast, color balance, and visibility of details. The RGB representation provides a straightforward interpretation of color intensity, while the HSV model separates hue, saturation, and value components, enabling targeted adjustments. Additionally, the YCbCr color space separates luminance and chrominance, offering further flexibility in image enhancement. Experimental results demonstrate the effectiveness of the proposed method in mitigating haze and enhancing image quality across various scenes and lighting conditions. Overall, the integration of histogram methods with color space conversions presents a robust approach for addressing hazy conditions and improving image visibility. Experimental findings indicate that the suggested strategy is successful at reducing haze and improving image quality over a wide range of situations and lighting conditions. Overall, combining histogram approaches with color space conversions provides a strong strategy to tackling foggy situations and boosting picture visibility.