Remote Sensing Single Image Dehazing With Histogram Degradation Representation Learning
Zhongmin Zhu, Tianyu Song, Yusi Huang, Weitao Han, Xinghui Xia, Ling Ma · IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing · 2025
Haze severely degrades the visual quality of remote sensing images, resulting in the loss of fine details and reduced reliability in subsequent tasks. Although Transformer-based approaches have recently achieved notable progress in image dehazing, most existing methods mitigate computational complexity by constraining self-attention to channel interactions or fixed spatial regions. This design inevitably restricts their capacity to capture long-range spatial dependencies, which are crucial for effectively modeling haze distributions. To address this limitation, we propose a Histogram-guided Remote Sensing Image Dehazing Transformer (HRSformer). The framework is built upon two key components: a Histogram Degradation Representation Block (HDRB), which dynamically selects spatial feature ranges and collaboratively processes distant pixels with similar degradation patterns to suppress global haze interference, and a Multi-Expert Modulation Block (MEMB), which employs a mixture-of-experts strategy to adaptively capture multi-scale and multi-granularity contextual features in a sample-specific manner, thereby improving the reconstruction of fine local textures. Extensive experiments on multiple public remote sensing dehazing benchmarks demonstrate that HRSformer consistently outperforms state-of-the-art methods in both quantitative metrics and visual quality, validating its effectiveness and superiority.