PromptHaze: Prompting Real-world Dehazing via Depth Anything Model - Ye, Tian; Chen, Sixiang; Chen, Haoyu; Chai, Wenhao; Ren, Jingjing; Xing, Zhaohu; Li, Wenxue; Zhu, Lei (Proceedings of the AAAI Conference on Artificial Intelligence, 2025)
Real-world image dehazing remains a challenging task due to the diverse nature of haze degradation and the lack of large-scale paired datasets. Existing methods based on hand-crafted priors or generative priors struggle to recover accurate backgrounds and fine details from dense haze regions. In this work, we propose a novel paradigm, PromptHaze, for real-world image dehazing via the depth prompt from the Depth Anything model. By employing a prompt-by-prompt strategy, our method iteratively updates the depth prompt and progressively restores the background through a dehazing network with controllable dehazing strength. Extensive experiments on widely-used real-world dehazing benchmarks demonstrate the superiority of PromptHaze in recovering authentic backgrounds and fine details from various haze scenes, outperforming state-of-the-art methods across multiple quality metrics.