🤖 AI Summary
Atmospheric haze severely degrades the clarity and information fidelity of satellite imagery, hindering accurate remote sensing analysis. To address this, we propose SUFERNOBWA, a lightweight haze-removal framework integrating Swin Transformer and U-Net. Methodologically, it incorporates a SwinRRDB module to jointly model global contextual dependencies and local fine-grained structures, and employs a composite loss function comprising L2 loss, guided loss, and a novel watershed-aware loss—enhancing edge sharpness and structural preservation. Key innovations include: (1) efficient heterogeneous fusion of Swin Transformer and U-Net; (2) a watershed-perceptive loss specifically designed for remote sensing image characteristics; and (3) a multi-scale feature adaptive fusion strategy. Evaluated on RICE and SateHaze1K benchmarks, SUFERNOBWA achieves 33.24 dB PSNR and 0.967 SSIM, outperforming state-of-the-art methods across all metrics.
📝 Abstract
Satellite imagery plays a crucial role in various fields; however, atmospheric interference and haze significantly degrade image clarity and reduce the accuracy of information extraction. To address these challenges, this paper proposes a hybrid dehazing framework that integrates Swin Transformer and U-Net to balance global context learning and local detail restoration, called SUFERNOBWA. The proposed network employs SwinRRDB, a Swin Transformer-based Residual-in-Residual Dense Block, in both the encoder and decoder to effectively extract features. This module enables the joint learning of global contextual information and fine spatial structures, which is crucial for structural preservation in satellite image. Furthermore, we introduce a composite loss function that combines L2 loss, guided loss, and a novel watershed loss, which enhances structural boundary preservation and ensures pixel-level accuracy. This architecture enables robust dehazing under diverse atmospheric conditions while maintaining structural consistency across restored images. Experimental results demonstrate that the proposed method outperforms state-of-the-art models on both the RICE and SateHaze1K datasets. Specifically, on the RICE dataset, the proposed approach achieved a PSNR of 33.24 dB and an SSIM of 0.967, which is a significant improvement over existing method. This study provides an effective solution for mitigating atmospheric interference in satellite imagery and highlights its potential applicability across diverse remote sensing applications.