DSPI-Net: a dual-stream perception interaction network for lightweight remote sensing image dehazing

Abstract

Remote sensing (RS) image dehazing seeks to eliminate complex and non-uniform haze interference, enabling high-quality image restoration. However, existing methods often face difficulties in balancing adaptability to local haze variations and spatial heterogeneity with computational efficiency. To address these challenges, we propose a novel dual-stream perception interaction network, termed DSPI-Net, which dynamically integrates low-level spatial features and multi-scale features to efficiently restore realistic RS images. The proposed network adopts a lightweight local feature extraction module to efficiently capture shallow spatial details and texture information. Meanwhile, a high-resolution hybrid attention Transformer block utilizes a bidirectional interaction mechanism to capture local details and global contextual information in parallel, producing multi-scale features. To address the intrinsic complexities of hazy RS images, a gated group fusion mechanism adaptively fuses local and global features through an adaptive gating mechanism, achieving optimal feature integration and effectively addressing representation challenges posed by local haze variations and spatial irregularities. Extensive experiments demonstrate that DSPI-Net achieves an average peak signal-to-noise ratio improvement of approximately 0.34 dB over the respective second-best methods across four RS dehazing benchmark datasets, while containing only 0.37 M parameters, corresponding to approximately 24.0% of the parameter count of PCSformer-S. Its lightweight design ensures suitability for implementation on resource-constrained edge devices, meeting real-time processing requirements.