Remote sensing images (RSIs) are frequently degraded by atmospheric haze, which introduces color distortion and contrast reduction, thereby impeding downstream applications. Existing models often struggle with non-uniform haze distributions, high computational costs, and the loss of local texture details. To address these challenges, this paper proposes a lightweight Feature Self-Recalibration Network (FSRNet) for efficient remote sensing image dehazing. FSRNet adopts a symmetric encoder–decoder architecture as its backbone and utilizes parameter-free pixel shuffle and unshuffle operations for multiscale feature resampling to preserve complex spatial details. The core of FSRNet lies in the specially designed Feature Self-Recalibration Module (FSRM), which consists of two key components: the Dual-Stage Feature Calibration Block (DFCB) and the Hierarchical Context Aggregation Block (HCAB). Specifically, the DFCB statistically splits features into informative and redundant parts, independently recalibrating them through a simplified channel attention mechanism to enhance representation in heterogeneous haze regions. Simultaneously, the HCAB integrates a non-local haze perception branch and a local detail enhancement branch in parallel, enabling the model to perceive global haze density while preserving fine-grained textures. Experimental results on multiple authoritative synthetic and real-world remote sensing datasets demonstrate that FSRNet achieves state-of-the-art dehazing performance. With only 0.865 M parameters and 8.622 G MACs, FSRNet strikes a superior balance between restoration quality and computational efficiency, making it highly suitable for real-time deployment on resource-constrained platforms.
He et al. (Wed,) studied this question.
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