An Evaluation Study of Transformer-Based Dehazing Models on Real World Datasets

Abstract

The presence of haze significantly reduces the visibility of outdoor scenes, which not only degrades the performance of many computer vision tasks but also compromises safety and reliable decision-making in practical systems. In recent years, transformer-based models have demonstrated remarkable performance in single-image dehazing by effectively capturing long-range dependencies and global context. However, their effectiveness under real-world haze conditions remains largely unexplored, as most models are trained and evaluated on synthetic or artificially generated datasets. To address this limitation, we evaluate two state-of-the-art transformer-based dehazing models, DehazeFormer and UMWTFormer, on both real-world datasets (LM-Haze, RW-Haze and RTTS) and synthetic datasets (Dense- Haze, RESIDE-OUT, O-HAZE and NH-HAZE), and analyse their performance using NIQE and BRISQUE scores. Experimental results reveal a substantial performance drop, with average dehazing effectiveness reduced by 40% on real-image datasets compared to synthetic ones. This performance gap highlights a critical limitation in current dehazing models, suggesting overfitting to synthetic data distributions and limited generalisation to real-world complexities. These findings underscore the need for new models or training strategies explicitly designed to cope with the variability and challenges inherent in real-world hazy images.