# Guided Real Image Dehazing using YCbCr Color Space

arXiv: https://arxiv.org/abs/2412.17496 · HTML: https://mlsanity.com/paper/2412.17496/

## Key facts

- Cited by 22 papers in the map (data through 14 Oct 2026).
- Builds on 1 of its 45 references that are in the map.
- First posted on arXiv 23 Dec 2024.

## Builds on (most cited first, 1 of 1)

- [Non-aligned supervision for Real Image Dehazing](https://mlsanity.com/paper/2303.04940/) (2023)

## Built on by (most cited first, 22 of 22)

- [Adaptive Perception for Unified Visual Multi-modal Object Tracking](https://mlsanity.com/paper/2502.06583/) (2025)
- [M4-SAR: A Multi-Resolution, Multi-Polarization, Multi-Scene, Multi-Source Dataset and Benchmark for optical-SAR Object Detection](https://mlsanity.com/paper/2505.10931/) (2025)
- [When Schr"odinger Bridge Meets Real-World Image Dehazing with Unpaired Training](https://mlsanity.com/paper/2507.09524/) (2025)
- [PHATNet: A Physics-guided Haze Transfer Network for Domain-adaptive Real-world Image Dehazing](https://mlsanity.com/paper/2507.14826/) (2025)
- [UDPNet: Unleashing Depth-based Priors for Robust Image Dehazing](https://mlsanity.com/paper/2601.06909/) (2026)
- [Advances in Artificial Intelligence: A Review for the Creative Industries](https://mlsanity.com/paper/2501.02725/) (2025)
- [DAGNet: A Dual-View Attention-Guided Network for Efficient X-ray Security Inspection](https://mlsanity.com/paper/2502.01710/) (2025)
- [Physics-Guided Image Dehazing Diffusion](https://mlsanity.com/paper/2504.21385/) (2025)
- [WeatherCycle: Unpaired Multi-Weather Restoration via Color Space Decoupled Cycle Learning](https://mlsanity.com/paper/2509.23150/) (2025)
- [Remote Sensing Image Dehazing: A Systematic Review of Progress, Challenges, and Prospects](https://mlsanity.com/paper/2603.20289/) (2026)
- [PRISM: Rethinking Atmospheric Scattering Reconstruction as a Unified Understanding and Restoration Model for Real-world Dehazing](https://mlsanity.com/paper/2604.07048/) (2026)
- [HNDiff: Haze-Noise Diffusion for Image Dehazing](https://mlsanity.com/paper/2608.10995/) (2026)
- [Cross-Layer Feature Self-Attention Module for Multi-Scale Object Detection](https://mlsanity.com/paper/2510.14726/) (2025)
- [Learning Implicit Neural Degradation Representation for Unpaired Image Dehazing](https://mlsanity.com/paper/2511.13110/) (2025)
- [Rethinking Surgical Smoke: A Smoke-Type-Aware Laparoscopic Video Desmoking Method and Dataset](https://mlsanity.com/paper/2512.02780/) (2025)
- [Nighttime Hazy Image Enhancement via Progressively and Mutually Reinforcing Night-Haze Priors](https://mlsanity.com/paper/2601.01998/) (2026)
- [YCDa: YCbCr Decoupled Attention for Real-time Realistic Camouflaged Object Detection](https://mlsanity.com/paper/2603.01602/) (2026)
- [ZID-Net: Zero-Inference Diffusion Prior Decoupling Network for Single Image Dehazing](https://mlsanity.com/paper/2604.23709/) (2026)
- [DDTNet: Degradation Disentanglement and Transfer Network for Test-Time All-in-One De-weathering Adaptation](https://mlsanity.com/paper/2606.16298/) (2026)
- [Efficient Real-World Dehazing via Physics-Inspired Global-Local Decoupling](https://mlsanity.com/paper/2606.25732/) (2026)
- [Backbone-Agnostic Stochastic Perturbation Learning for End-to-End Real-World Image Dehazing](https://mlsanity.com/paper/2607.11623/) (2026)
- [Degraded Infrared Small Object Detection via Degradation-Adapted Physics-Guided Restoration](https://mlsanity.com/paper/2608.09311/) (2026)

## Abstract (arXiv)

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 (RW2AH) 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: textcolorblueurlhttps://github.com/fiwy0527/AAAI25SGDN.