Demonstrates HazeDiff’s adaptability in extreme scenarios.

<p>Including heavy rain, blizzards, thick fog, nighttime haze, and other challenging conditions, Images (a) and (c) are captured under various adverse weather conditions; images (b) and (d) show the results after image restoration using HazeDiff.</p>

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Bibliographic Details
Main Author:	Xiaoxia Lin (9165920) (author)
Other Authors:	Zhengao Li (4819893) (author), Dawei Huang (250465) (author), Wancheng Feng (21453184) (author), XinJun An (22513443) (author), Lin Sun (105539) (author), Niuzhen Yu (22513446) (author), Yan Li (23143) (author), Chunwei Leng (22513449) (author)
Published:	2025
Subjects:	Space Science Biological Sciences not elsewhere classified Information Systems not elsewhere classified unreasonable energy structure unlike existing approaches significant emissions generated remains particularly challenging reducing detail loss reducing computational costs reduces computational costs recurrent haze phenomena pivotal research frontier natural visual features level visual tasks level feature representation level feature inject key structural features g ., psnr g ., niqe experimental results demonstrate current environmental context conducted comprehensive experiments clear structural content achieving higher scores art dehazing methods degraded image contrast positioned image dehazing image dehazing research dehazing restoration results hazediff surpasses state structure retention model clean training datasets dehazing image dehazing effect restoration process diffusion model synthetic datasets different datasets reference image xlink "> transportation activities reduced resolution quality images propose hazediff novel perspective method provides initial noise generalization ability free diffusion evaluation metrics enhancing generalization effectively guiding diffusion process
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Demonstrates HazeDiff’s adaptability in extreme scenarios.

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