Guided Real Image Dehazing using YCbCr Color Space
Wenxuan Fang, Junkai Fan, Yu Zheng, Jiangwei Weng, Ying Tai, and Jun, Li

TL;DR
This paper introduces a novel dehazing network that leverages YCbCr color space features to improve haze removal in real-world images, supported by a new dataset and outperforming existing methods.
Contribution
The paper proposes the Structure Guided Dehazing Network (SGDN) utilizing YCbCr features and introduces the RW$^2$AH dataset for real-world haze dehazing.
Findings
SGDN outperforms state-of-the-art methods on real-world haze datasets.
Utilizing YCbCr features enhances structural guidance in dehazing.
The RW$^2$AH dataset provides diverse real-world haze images for training and evaluation.
Abstract
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…
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Taxonomy
TopicsImage Enhancement Techniques · Fire Detection and Safety Systems · Video Surveillance and Tracking Methods
MethodsSoftmax · Attention Is All You Need
