Task-Guided Prompting for Unified Remote Sensing Image Restoration

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 https://github.com/huangwenwenlili/TGPNet.

Figures (11)

• Figure 1: Conceptual comparison of different RSIR paradigms. (a) Examples of real-world degradations across diverse modalities, including optical (RGB/Multispectral), Synthetic Aperture Radar (SAR), and Thermal Infrared (TIR). (b) Conventional single-task restoration relies on specialized networks (e.g., $E_N, D_N$ for denoising) for each degradation type. (c) Our unified approach employs a single, prompt-guided network ($E_{\text{all}}, D_{\text{all}}$) to handle diverse tasks, including denoising, deshadowing, declouding, despeckling, and deblurring. (d) The corresponding high-quality restored images.
• Figure 2: Architecture of the proposed Task-Guided Prompting Network (TGPNet) for unified remote sensing image restoration. (a) The overall architecture consists of an encoder-decoder backbone augmented by our Task-Guided Prompting (TGP) module, which is composed of Learnable Task-Specific Embedding (LTSE) and Hierarchical Feature Modulation (HFM). (b) The LTSE module processes a task-specific prompt ($F_{TP}$) to produce a degradation-aware embedding ($F_{E}$). (c) The HFM module uses this embedding to generate stage-wise affine parameters (scaling factors $\gamma$ and shifting offsets $\beta$), which modulate the hierarchical features ($F_{Di}$) at each stage of the decoder.
• Figure 3: Visual comparison of restored images for four degradation types on our URSIR benchmark. Key local details (in green boxes) are highlighted in red boxes, and zooming in is recommended for detailed inspection.
• Figure 4: Visual comparison of restored images for multispectral declouding on SEN12MS-CR and thermal deblurring on HIT-UAV. Key local details (in green boxes) are highlighted in red boxes, and zooming in is recommended for detailed inspection.
• Figure 5: Visual evaluation of TGPNet on unseen real-world imagery from the WHU-Shadow dataset luo2020deeply, demonstrating generalization to authentic remote sensing shadows.