Kavli Affiliate: Jing Wang
| First 5 Authors: Tianwen Zhou, Jing Wang, Songtao Wu, Kuanhong Xu,
| Summary:
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 at https://github.com/TianwenZhou/ProDehaze.
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