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arXiv · 2511.09055

4KDehazeFlow: Ultra-High-Definition Image Dehazing via Flow Matching

Xingchi Chen·Pu Wang·Xuerui Li·Chaopeng Li·Juxiang Zhou·Jianhou Gan·Dianjie Lu·Guijuan Zhang·Wenqi Ren·Zhuoran Zheng
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Abstract

Ultra-High-Definition (UHD) image dehazing faces challenges such as limited scene adaptability in prior-based methods and high computational complexity with color distortion in deep learning approaches. To address these issues, we propose 4KDehazeFlow, a novel method based on Flow Matching and the Haze-Aware vector field. This method models the dehazing process as a progressive optimization of continuous vector field flow, providing efficient data-driven adaptive nonlinear color transformation for high-quality dehazing. Specifically, our method has the following advantages: 1) 4KDehazeFlow is a general method compatible with various deep learning networks, without relying on any specific network architecture. 2) We propose a learnable 3D lookup table (LUT) that encodes haze transformation parameters into a compact 3D mapping matrix, enabling efficient inference through precomputed mappings. 3) We utilize a fourth-order Runge-Kutta (RK4) ordinary differential equation (ODE) solver to stably solve the dehazing flow field through an accurate step-by-step iterative method, effectively suppressing artifacts. Extensive experiments show that 4KDehazeFlow exceeds seven state-of-the-art methods. It delivers a 2dB PSNR increase and better performance in dense haze and color fidelity.

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Xingchi Chen, Pu Wang, Xuerui Li, Chaopeng Li, Juxiang Zhou, Jianhou Gan, Dianjie Lu, Guijuan Zhang, Wenqi Ren, Zhuoran Zheng. 2025-11-12. 4KDehazeFlow: Ultra-High-Definition Image Dehazing via Flow Matching. https://arxiv.org/abs/2511.09055

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