This is the official Pytorch/Pytorch implementation of the paper:
Real-World Remote Sensing Image Dehazing: Benchmark and Baseline
Zeng-Hui Zhu†, Wei Lu†, Si-Bao Chen*, Chris H. Q. Ding, Jin Tang, and Bin Luo, Senior Member, IEEE
IEEE Transactions on Geoscience and Remote Sensing (TGRS). arXiv, TGRS.
Visual comparison of synthetic and real-world RS hazy images. (a) Synthetic hazy images from the RS-HAZE dataset. (b) Real-world hazy images from our RRSHID dataset, highlighting complex color variations.
Abstract
Remote Sensing Image Dehazing (RSID) poses significant challenges in real-world scenarios due to the complex atmospheric conditions and severe color distortions that degrade image quality. The scarcity of real-world remote sensing hazy image pairs has compelled existing methods to rely primarily on synthetic datasets. However, these methods struggle with real-world applications due to the inherent domain gap between synthetic and real data. To address this, we introduce Real-World Remote Sensing Hazy Image Dataset (RRSHID), the first large-scale dataset featuring real-world hazy and dehazed image pairs across diverse atmospheric conditions. Based on this, we propose MCAF-Net, a novel framework tailored for real-world RSID. Its effectiveness arises from three innovative components: Multi-branch Feature Integration Block Aggregator (MFIBA), which enables robust feature extraction through cascaded integration blocks and parallel multi-branch processing; Color-Calibrated Self-Supervised Attention Module (CSAM), which mitigates complex color distortions via self-supervised learning and attention-guided refinement; and Multi-Scale Feature Adaptive Fusion Module (MFAFM), which integrates features effectively while preserving local details and global context. Extensive experiments validate that MCAF-Net demonstrates state-of-the-art performance in real-world RSID, while maintaining competitive performance on synthetic datasets. The introduction of RRSHID and MCAF-Net sets new benchmarks for real-world RSID research, advancing practical solutions for this complex task.The code will be available.
The dataset can be downloaded at Baidu netdisk(Password: CV21) or Github or Hugging Face or Google Drive.
If you have any questions about this work, you can contact me.
Email: luwei_ahu@qq.com; WeChat: lw2858191255.
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If RRSHID is useful or relevant to your research, please kindly recognize our contributions by citing our paper:
@ARTICLE{11058953,
author={Zhu, Zeng-Hui and Lu, Wei and Chen, Si-Bao and Ding, Chris H. Q. and Tang, Jin and Luo, Bin},
journal={IEEE Transactions on Geoscience and Remote Sensing},
title={Real-World Remote Sensing Image Dehazing: Benchmark and Baseline},
year={2025},
volume={},
number={},
pages={1-1}
}
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