Recent approaches using large-scale pretrained diffusion models for image dehazing improve perceptual quality but often suffer from hallucination issues, producing unfaithful dehazed image to the original one. To mitigate this, we propose ProDehaze, a framework that employs internal image priors to direct external priors encoded in pretrained models. We introduce two types of \textit{selective} internal priors that prompt the model to concentrate on critical image areas: a Structure-Prompted Restorer in the latent space that emphasizes structure-rich regions, and a Haze-Aware Self-Correcting Refiner in the decoding process to align distributions between clearer input regions and the output. Extensive experiments on real-world datasets demonstrate that ProDehaze achieves high-fidelity results in image dehazing, particularly in reducing color shifts. Our code is atthis https URL.
View on arXiv@article{zhou2025_2503.17488,
title={ ProDehaze: Prompting Diffusion Models Toward Faithful Image Dehazing },
author={ Tianwen Zhou and Jing Wang and Songtao Wu and Kuanhong Xu },
journal={arXiv preprint arXiv:2503.17488},
year={ 2025 }
}