Task-Guided Prompting for Unified Remote Sensing Image Restoration

• URL: http://arxiv.org/abs/2604.02742v1
• Date: Fri, 03 Apr 2026 05:29:12 GMT
• Title: Task-Guided Prompting for Unified Remote Sensing Image Restoration
• Abstract summary: TGPNet is a unified framework capable of handling denoising, cloud removal, shadow removal, deblurring, and SAR despeckling.<n>The core of our framework is a novel Task-Guided Prompting (TGP) strategy.<n>This work presents a significant advancement for multi-task RSIR, offering a practical and scalable solution for operational pipelines.
• Score: 19.11145604183771
• License: http://creativecommons.org/licenses/by/4.0/
• 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.

Related papers

• Divide and Restore: A Modular Task-Decoupled Framework for Universal Image Restoration [0.0]
  Restoring images affected by various types of degradation remains a significant challenge in computer vision.<n>We propose a modular, task-decoupled image restoration framework based on an explicit diagnostic routing mechanism.
  arXiv  Detail & Related papers  (2026-03-30T16:45:16Z)
• A Feature Shuffling and Restoration Strategy for Universal Unsupervised Anomaly Detection [19.296849469059456]
  Unsupervised anomaly detection is vital in industrial fields, with reconstruction-based methods favored for their simplicity and effectiveness.<n>We introduce a novel framework for universal anomaly detection: ulineFeature ulineShuffling and ulineRestoration (FSR)<n>FSR employs multi-scale features with rich semantic information as reconstruction targets, rather than raw image pixels.
  arXiv  Detail & Related papers  (2026-03-24T07:04:20Z)
• Unleashing Degradation-Carrying Features in Symmetric U-Net: Simpler and Stronger Baselines for All-in-One Image Restoration [52.82397287366076]
  All-in-one image restoration aims to handle diverse degradations (e.g., noise, blur, adverse weather) within a unified framework.<n>In this work, we reveal a critical insight: well-crafted feature extraction inherently encodes degradation-carrying information.<n>Our symmetric design preserves intrinsic degradation signals robustly, rendering simple additive fusion in skip connections.
  arXiv  Detail & Related papers  (2025-12-11T12:20:31Z)
• Learning to Restore Multi-Degraded Images via Ingredient Decoupling and Task-Aware Path Adaptation [51.10017611491389]
  Real-world images often suffer from multiple coexisting degradations, such as rain, noise, and haze coexisting in a single image.<n>We propose an adaptive multi-degradation image restoration network that reconstructs images by leveraging decoupled representations of degradation ingredients.<n>The resulting tightly integrated architecture, termed IMDNet, is extensively validated through experiments.
  arXiv  Detail & Related papers  (2025-11-07T01:50:36Z)
• From Controlled Scenarios to Real-World: Cross-Domain Degradation Pattern Matching for All-in-One Image Restoration [2.997052569698842]
  All-in-One Image Restoration (AiOIR) aims to achieve image restoration caused by multiple degradation patterns via a single model with unified parameters.<n>UDAIR framework is proposed to effectively achieve AiOIR by leveraging the learned knowledge from source domain to target domain.<n> Experimental results on 10 open-source datasets demonstrate that UDAIR achieves new state-of-the-art performance for the AiOIR task.
  arXiv  Detail & Related papers  (2025-05-28T12:22:00Z)
• Manifold-aware Representation Learning for Degradation-agnostic Image Restoration [135.90908995927194]
  Image Restoration (IR) aims to recover high quality images from degraded inputs affected by various corruptions such as noise, blur, haze, rain, and low light conditions.<n>We present MIRAGE, a unified framework for all in one IR that explicitly decomposes the input feature space into three semantically aligned parallel branches.<n>This modular decomposition significantly improves generalization and efficiency across diverse degradations.
  arXiv  Detail & Related papers  (2025-05-24T12:52:10Z)
• UniUIR: Considering Underwater Image Restoration as An All-in-One Learner [62.65503609562905]
  We propose a Universal Underwater Image Restoration method, termed as UniUIR.<n>To decouple degradation-specific issues and explore the inter-correlations among various degradations in UIR task, we designed the Mamba Mixture-of-Experts module.<n>This module extracts degradation prior information in both spatial and frequency domains, and adaptively selects the most appropriate task-specific prompts.
  arXiv  Detail & Related papers  (2025-01-22T16:10:42Z)
• DRM-IR: Task-Adaptive Deep Unfolding Network for All-In-One Image Restoration [5.573836220587265]
  This work proposes an efficient Dynamic Reference Modeling paradigm (DRM-IR) DRM-IR consists of task-adaptive degradation modeling and model-based image restoring. Experiments on multiple benchmark datasets show that our DRM-IR achieves state-of-the-art in All-In-One IR.
  arXiv  Detail & Related papers  (2023-07-15T02:42:19Z)
• Spatially-Adaptive Image Restoration using Distortion-Guided Networks [51.89245800461537]
  We present a learning-based solution for restoring images suffering from spatially-varying degradations. We propose SPAIR, a network design that harnesses distortion-localization information and dynamically adjusts to difficult regions in the image.
  arXiv  Detail & Related papers  (2021-08-19T11:02:25Z)
• Conditional Meta-Network for Blind Super-Resolution with Multiple Degradations [23.026779510800047]
  Single-image super-resolution (SISR) methods suffer performance drop with multiple degrading effects in real scenarios. We propose a conditional meta-network framework (named CMDSR) for the first time, which helps SR framework learn how to adapt to changes in input distribution.
  arXiv  Detail & Related papers  (2021-04-08T17:15:25Z)
• Style Normalization and Restitution for DomainGeneralization and Adaptation [88.86865069583149]
  An effective domain generalizable model is expected to learn feature representations that are both generalizable and discriminative. In this paper, we design a novel Style Normalization and Restitution module (SNR) to ensure both high generalization and discrimination capability of the networks.
  arXiv  Detail & Related papers  (2021-01-03T09:01:39Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.