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 allows the network to precisely tailor its restoration process for distinct degradation patterns while maintaining a single set of shared weights. To validate our framework, we construct a unified RSIR benchmark covering RGB, multispectral, SAR, and thermal infrared modalities for five aforementioned restoration tasks. Experimental results demonstrate that TGPNet achieves state-of-the-art performance on both unified multi-task scenarios and unseen composite degradations, surpassing even specialized models in individual domains such as cloud removal. By successfully unifying heterogeneous degradation removal within a single adaptive framework, this work presents a significant advancement for multi-task RSIR, offering a practical and scalable solution for operational pipelines. The code and benchmark will be released at this https URL.
Abstract (translated)
遥感图像恢复（RSIR）对于从退化观测中复原高保真图像、实现准确的下游分析至关重要。然而，现有方法大多聚焦于同质数据中的单一退化类型，限制了其在真实场景中的实用性——真实场景中常存在跨越不同光谱波段或传感器模态的多种退化问题，形成了显著的操作瓶颈。为应对这一根本性缺口，我们提出了TGPNet，一个能够统一处理去噪、去云、去阴影、去模糊及SAR相干斑抑制的单一架构框架。该框架的核心是一种新颖的任务引导提示（TGP）策略：TGP利用可学习的任务特定嵌入生成退化感知提示，进而在解码器中逐层调制特征。这种任务自适应机制使网络能在保持单一共享权重的同时，针对不同退化模式精确定制其恢复过程。为验证框架有效性，我们构建了涵盖RGB、多光谱、SAR与热红外模态的统一RSIR基准，覆盖上述五项恢复任务。实验结果表明，TGPNet在统一多任务场景及未见复合退化任务中均达到最优性能，甚至在云去除等特定领域超越了专业模型。通过成功将异质退化去除整合于单一自适应框架，本研究为多任务RSIR带来重大进展，为实际应用流程提供了实用且可扩展的解决方案。代码与基准数据集将在此链接公开发布。
URL
https://arxiv.org/abs/2604.02742
