Remote Sensing Single Image Dehazing With Histogram Degradation Representation Learning
Year: 2025
Type: article
Abstract: 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.
Language: English
Topic: Image Enhancement Techniques
Subfield: Computer Vision and Pattern Recognition
Field: Computer Science
Domain: Physical Sciences
Open Access status: gold
Estimated APC paid: $1,800
OpenAlex ID: W7117148682