# Image Restoration for Remote Sensing: Overview and Toolbox

arXiv: https://arxiv.org/abs/2107.00557 · HTML: https://mlsanity.com/paper/2107.00557/

## Key facts

- Cited by 26 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 1 Jul 2021.

## Built on by (most cited first, 26 of 26)

- [Diffusion Models Meet Remote Sensing: Principles, Methods, and Perspectives](https://mlsanity.com/paper/2404.08926/) (2024)
- [Exploring Radar Data Representations in Autonomous Driving: A Comprehensive Review](https://mlsanity.com/paper/2312.04861/) (2023)
- [Image Processing and Machine Learning for Hyperspectral Unmixing: An Overview and the HySUPP Python Package](https://mlsanity.com/paper/2308.09375/) (2023)
- [Tensor Decompositions for Hyperspectral Data Processing in Remote Sensing: A Comprehensive Review](https://mlsanity.com/paper/2205.06407/) (2022)
- [RSAM-Seg: A SAM-based Approach with Prior Knowledge Integration for Remote Sensing Image Semantic Segmentation](https://mlsanity.com/paper/2402.19004/) (2024)
- [Source-free Domain Adaptive Object Detection in Remote Sensing Images](https://mlsanity.com/paper/2401.17916/) (2024)
- [HDMba: Hyperspectral Remote Sensing Imagery Dehazing with State Space Model](https://mlsanity.com/paper/2406.05700/) (2024)
- [DGSolver: Diffusion Generalist Solver with Universal Posterior Sampling for Image Restoration](https://mlsanity.com/paper/2504.21487/) (2025)
- [Connections between Deep Equilibrium and Sparse Representation Models with Application to Hyperspectral Image Denoising](https://mlsanity.com/paper/2203.15901/) (2022)
- [HyDe: The First Open-Source, Python-Based, GPU-Accelerated Hyperspectral Denoising Package](https://mlsanity.com/paper/2204.06979/) (2022)
- [DiNAT-IR: Exploring Dilated Neighborhood Attention for High-Quality Image Restoration](https://mlsanity.com/paper/2507.17892/) (2025)
- [Task-Guided Prompting for Unified Remote Sensing Image Restoration](https://mlsanity.com/paper/2604.02742/) (2026)
- [Multi-temporal speckle reduction with self-supervised deep neural networks](https://mlsanity.com/paper/2207.11095/) (2022)
- [A Deep Unrolling Model with Hybrid Optimization Structure for Hyperspectral Image Deconvolution](https://mlsanity.com/paper/2306.06378/) (2023)
- [Just Project! Multi-Channel Despeckling, the Easy Way](https://mlsanity.com/paper/2408.11531/) (2024)
- [Degradation-Aware Metric Prompting for Hyperspectral Image Restoration](https://mlsanity.com/paper/2512.20251/) (2025)
- [Remote Sensing Image Dehazing: A Systematic Review of Progress, Challenges, and Prospects](https://mlsanity.com/paper/2603.20289/) (2026)
- [Learning to Recorrupt: Noise Distribution Agnostic Self-Supervised Image Denoising](https://mlsanity.com/paper/2603.25869/) (2026)
- [RAPID: A Reproducible Multi-Agent Pipeline for Interpretable Disaster Damage Assessment from Satellite and Street-View Imagery](https://mlsanity.com/paper/2606.21819/) (2026)
- [MIGA: Mutual Information-Guided Attack on Denoising Models for Semantic Manipulation](https://mlsanity.com/paper/2503.06966/) (2025)
- [RouteWinFormer: A Route-Window Transformer for Middle-range Attention in Image Restoration](https://mlsanity.com/paper/2504.16637/) (2025)
- [Efficient Few-Shot Learning in Remote Sensing: Fusing Vision and Vision-Language Models](https://mlsanity.com/paper/2510.13993/) (2025)
- [Spatial-Spectral Adaptive Fidelity and Noise Prior Reduction Guided Hyperspectral Image Denoising](https://mlsanity.com/paper/2604.12600/) (2026)
- [HIR-ALIGN: Enhancing Hyperspectral Image Restoration via Diffusion-Based Data Generation](https://mlsanity.com/paper/2605.13581/) (2026)
- [Aligning Network Equivariance with Data Symmetry: A Theoretical Framework and Adaptive Approach for Image Restoration](https://mlsanity.com/paper/2605.13744/) (2026)
- [EORestore-Agent: Fidelity-Guided Agentic Restoration of Remote Sensing Images with Composite Degradations](https://mlsanity.com/paper/2610.06196/) (2026)

## Abstract (arXiv)

Remote sensing provides valuable information about objects or areas from a distance in either active (e.g., RADAR and LiDAR) or passive (e.g., multispectral and hyperspectral) modes. The quality of data acquired by remotely sensed imaging sensors (both active and passive) is often degraded by a variety of noise types and artifacts. Image restoration, which is a vibrant field of research in the remote sensing community, is the task of recovering the true unknown image from the degraded observed image. Each imaging sensor induces unique noise types and artifacts into the observed image. This fact has led to the expansion of restoration techniques in different paths according to each sensor type. This review paper brings together the advances of image restoration techniques with particular focuses on synthetic aperture radar and hyperspectral images as the most active sub-fields of image restoration in the remote sensing community. We, therefore, provide a comprehensive, discipline-specific starting point for researchers at different levels (i.e., students, researchers, and senior researchers) willing to investigate the vibrant topic of data restoration by supplying sufficient detail and references. Additionally, this review paper accompanies a toolbox to provide a platform to encourage interested students and researchers in the field to further explore the restoration techniques and fast-forward the community. The toolboxes are provided in https://github.com/ImageRestorationToolbox.