HazeSpace2M: A Dataset for Haze Aware Single Image Dehazing
<|reference_start|>HazeSpace2M: A Dataset for Haze Aware Single Image Dehazing: Reducing the atmospheric haze and enhancing image clarity is crucial for computer vision applications. The lack of real-life hazy ground truth images necessitates synthetic datasets, which often lack diverse haze types, impeding effective haze type classification and dehazing algorithm selection. This research introduces the HazeSpace2M dataset, a collection of over 2 million images designed to enhance dehazing through haze type classification. HazeSpace2M includes diverse scenes with 10 haze intensity levels, featuring Fog, Cloud, and Environmental Haze (EH). Using the dataset, we introduce a technique of haze type classification followed by specialized dehazers to clear hazy images. Unlike conventional methods, our approach classifies haze types before applying type-specific dehazing, improving clarity in real-life hazy images. Benchmarking with state-of-the-art (SOTA) models, ResNet50 and AlexNet achieve 92.75\% and 92.50\% accuracy, respectively, against existing synthetic datasets. However, these models achieve only 80% and 70% accuracy, respectively, against our Real Hazy Testset (RHT), highlighting the challenging nature of our HazeSpace2M dataset. Additional experiments show that haze type classification followed by specialized dehazing improves results by 2.41% in PSNR, 17.14% in SSIM, and 10.2\% in MSE over general dehazers. Moreover, when testing with SOTA dehazing models, we found that applying our proposed framework significantly improves their performance. These results underscore the significance of HazeSpace2M and our proposed framework in addressing atmospheric haze in multimedia processing. Complete code and dataset is available on \href{https://github.com/tanvirnwu/HazeSpace2M} {\textcolor{blue}{\textbf{GitHub}}}.<|reference_end|>
@article{islam2024hazespace2m:,
    title={HazeSpace2M: A Dataset for Haze Aware Single Image Dehazing},
    author={Md Tanvir Islam, Nasir Rahim, Saeed Anwar, Muhammad Saqib, Sambit
  Bakshi, Khan Muhammad},
    journal={ACM Multimedia 2024},
    year={2024},
    doi={10.1145/3664647.3681382},
    archivePrefix={arXiv},
    eprint={2409.17432},
    primaryClass={cs.CV}
}