Real-World Remote Sensing Image Dehazing: Benchmark and Baseline

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 (RS) 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 RS hazy image dataset (RRSHID), the first large-scale dataset featuring real-world hazy and hazy-free 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: multibranch feature integration block aggregator (MFIBA), which enables robust feature extraction through cascaded integration blocks and parallel multibranch processing; color-calibrated self-supervised attention module (CSAM), which mitigates complex color distortions via self-supervised learning and attention-guided refinement; and multiscale 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 (SOTA) 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 and dataset are publicly available at https://github.com/lwCVer/RRSHID