CCD-Net: Color-Correction Network Based on Dual-Branch Fusion of Different Color Spaces for Image Dehazing

Image dehazing is a crucial task in computer vision, aimed at restoring the clarity of images impacted by atmospheric conditions like fog, haze, or smog, which degrade image quality by reducing contrast, color fidelity, and detail. Recent advancements in deep learning, particularly convolutional neural networks (CNNs), have shown significant improvements by directly learning features from hazy images to produce clear outputs. However, color distortion remains an issue, as many methods focus on contrast and clarity without adequately addressing color restoration. To overcome this, we propose a Color-Correction Network (CCD-Net) based on dual-branch fusion of different color spaces for image dehazing, that simultaneously handles image dehazing and color correction. The dehazing branch utilizes an encoder–decoder structure aimed at restoring haze-affected images. Unlike conventional methods that primarily focus on haze removal, our approach explicitly incorporates a dedicated color-correction branch in the Lab color space, ensuring both clarity enhancement and accurate color restoration. Additionally, we integrate attention mechanisms to enhance feature extraction and introduce a novel fusion loss function that combines loss in both RGB and Lab spaces, achieving a balance between structural preservation and color fidelity. The experimental results demonstrate that CCD-Net outperforms existing methods in both dehazing performance and color accuracy, with CIEDE reduced by 40.81% on RESIDE-indoor and 45.57% on RESIDE-6K compared to the second-best-performing model, showcasing its superior color-restoration capability.