用YCbCr空间提升真实雾霾图像去雾效果
Guided Real Image Dehazing using YCbCr Color Space
- 利用YCbCr的结构特性引导RGB通道恢复纹理
- 在多个真实雾霾数据集上优于现有方法
- 适合需要高质量去雾的实际应用
基于学习的图像去雾方法在实际应用中备受关注，但仅依赖RGB空间常残留雾霾。主要原因在于：从雾霾RGB图像中难以提取清晰纹理特征，且在非受控环境（如烟雾场景）中难以获取真实雾霾/清晰图像对。为此，提出一种结构引导去雾网络SGDN，利用YCbCr空间的优越结构特性。该网络包含双色引导桥（BGB）和色彩增强模块（CEM）。BGB结合相位融合与交互注意力机制，利用YCbCr丰富的纹理信息引导RGB通道，在频域与空域恢复更清晰特征；为保持色调一致性，CEM通过聚合YCbCr通道信息增强RGB特征的色彩感知。此外，构建了涵盖多种地理与气候条件的真实世界对齐雾霾数据集RW²AH。实验表明，本方法在多个真实雾霾数据集上超越现有先进方法。代码与数据集：https://github.com/fiwy0527/AAAI25_SGDN。
原文摘要 · Abstract (English)
Image dehazing, particularly with learning-based methods, has gained significant attention due to its importance in real-world applications. However, relying solely on the RGB color space often fall short, frequently leaving residual haze. This arises from two main issues: the difficulty in obtaining clear textural features from hazy RGB images and the complexity of acquiring real haze/clean image pairs outside controlled environments like smoke-filled scenes. To address these issues, we first propose a novel Structure Guided Dehazing Network (SGDN) that leverages the superior structural properties of YCbCr features over RGB. It comprises two key modules: Bi-Color Guidance Bridge (BGB) and Color Enhancement Module (CEM). BGB integrates a phase integration module and an interactive attention module, utilizing the rich texture features of the YCbCr space to guide the RGB space, thereby recovering clearer features in both frequency and spatial domains. To maintain tonal consistency, CEM further enhances the color perception of RGB features by aggregating YCbCr channel information. Furthermore, for effective supervised learning, we introduce a Real-World Well-Aligned Haze (RW$^2$AH) dataset, which includes a diverse range of scenes from various geographical regions and climate conditions. Experimental results demonstrate that our method surpasses existing state-of-the-art methods across multiple real-world smoke/haze datasets. Code and Dataset: \textcolor{blue}{\url{https://github.com/fiwy0527/AAAI25_SGDN.}}
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