Tri-scanning state-space model with multi-expert modulation for remote sensing image dehazing
TL;DR
TEMamba is presented, a tri-scanning state-space model with multi-expert modulation for remote sensing image dehazing, which converts feature representations into complementary scanning sequences along horizontal, vertical, and channel-related directions and achieves competitive restoration performance compared with existing state-of-the-art approaches.
Abstract
Abstract. Remote sensing images captured under haze, mist, or thin-cloud conditions usually suffer from contrast attenuation, detail blurring, and spectral distortion, which reduces image interpretability and weakens the reliability of subsequent quantitative applications. Although CNN and Transformer-based restoration methods have achieved notable progress, CNN models are limited in global context perception, whereas Transformer architectures generally introduce considerable computational overhead when processing high-resolution remote sensing images. State-space models, especially Mamba, provide an efficient solution for long-sequence modeling with linear complexity. Nevertheless, existing Mamba-based restoration frameworks are still insufficient in jointly representing spatial structures, channel correlations, and degradation-adaptive feature responses. To address these limitations, we present TEMamba, a tri-scanning state-space model with multi-expert modulation for remote sensing image dehazing. The proposed network introduces a tri-scanning state-space block, which converts feature representations into complementary scanning sequences along horizontal, vertical, and channel-related directions. In this way, the model can better capture long-range spatial continuity, inter-channel dependency, and nonuniform haze distribution. Moreover, a multi-expert-driven aggregator is designed to dynamically integrate discriminative spatial and channel representations, enabling adaptive feature refinement under heterogeneous degradation conditions. In addition, a multidomain joint optimization objective is employed to constrain the reconstruction process from pixel, edge, and frequency perspectives, thereby improving structural preservation and spectral consistency. Experiments on representative remote sensing dehazing benchmarks demonstrate that the proposed method achieves competitive restoration performance compared with existing state-of-the-art approaches.