TL;DR
TGPNet is a unified, task-guided framework for remote sensing image restoration that effectively handles multiple degradation types across diverse modalities, outperforming specialized models.
Contribution
Introduces TGPNet, a novel unified architecture with task-guided prompting for multi-task remote sensing image restoration across various modalities.
Findings
Achieves state-of-the-art results on multi-task RSIR benchmarks.
Effectively handles unseen composite degradations.
Outperforms specialized models in individual restoration tasks.
Abstract
Remote sensing image restoration (RSIR) is essential for recovering high-fidelity imagery from degraded observations, enabling accurate downstream analysis. However, most existing methods focus on single degradation types within homogeneous data, restricting their practicality in real-world scenarios where multiple degradations often across diverse spectral bands or sensor modalities, creating a significant operational bottleneck. To address this fundamental gap, we propose TGPNet, a unified framework capable of handling denoising, cloud removal, shadow removal, deblurring, and SAR despeckling within a single, unified architecture. The core of our framework is a novel Task-Guided Prompting (TGP) strategy. TGP leverages learnable, task-specific embeddings to generate degradation-aware cues, which then hierarchically modulate features throughout the decoder. This task-adaptive mechanism…
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