mlsanityData to 7 Oct 2026

NeurOp-Diff:Continuous Remote Sensing Image Super-Resolution via Neural Operator Diffusion

arXiv 2501.09054 ↗First posted 15 Jan 2025eess.IV cs.GRcited by 3

On the map: Diffusion models → Inverse problems and image restoration

Abstract (arXiv)

Most publicly accessible remote sensing data suffer from low resolution, limiting their practical applications. To address this, we propose a diffusion model guided by neural operators for continuous remote sensing image super-resolution (NeurOp-Diff). Neural operators are used to learn resolution representations at arbitrary scales, encoding low-resolution (LR) images into high-dimensional features, which are then used as prior conditions to guide the diffusion model for denoising. This effectively addresses the artifacts and excessive smoothing issues present in existing super-resolution (SR) methods, enabling the generation of high-quality, continuous super-resolution images. Specifically, we adjust the super-resolution scale by a scaling factor s, allowing the model to adapt to different super-resolution magnifications. Furthermore, experiments on multiple datasets demonstrate the effectiveness of NeurOp-Diff. Our code is available at https://github.com/zerono000/NeurOp-Diff.

Key facts. Cited by 3 papers in the map (data through 7 Oct 2026). Placed in Inverse problems and image restoration, one of 14 directions in Diffusion models. Builds on 41 of its 57 references that are in the map. First posted on arXiv 15 Jan 2025.

Builds on

41 of 57 references.

Closest ancestors · 5

Same direction, newest first

  1. 2024Semantic Guided Large Scale Factor Remote Sensing Image Super-resolution with Generative Diffusion Prior11 May
  2. 2024Building Bridges across Spatial and Temporal Resolutions: Reference-Based Super-Resolution via Change Priors and Conditional Diffusion Model26 Mar
  3. 2023EDiffSR: An Efficient Diffusion Probabilistic Model for Remote Sensing Image Super-Resolution30 Oct
  4. 2023HSR-Diff:Hyperspectral Image Super-Resolution via Conditional Diffusion Models21 Jun
  5. 2023Implicit Diffusion Models for Continuous Super-Resolution29 Mar

Other references · 36

  1. 2017Attention Is All You Needcited by 59,932
  2. 2014Adam: A Method for Stochastic Optimizationcited by 36,960
  3. 2020An Image is Worth 16x16 Words: Transformers for Image Recognition at Scalecited by 29,915
  4. 2020Denoising Diffusion Probabilistic Modelscited by 20,851
  5. 2021High-Resolution Image Synthesis with Latent Diffusion Modelscited by 18,694
  6. 2021Swin Transformer: Hierarchical Vision Transformer using Shifted Windowscited by 12,082
  7. 2018The Unreasonable Effectiveness of Deep Features as a Perceptual Metriccited by 11,029
  8. 2020Denoising Diffusion Implicit Modelscited by 9,655
  9. 2015Deep Unsupervised Learning using Nonequilibrium Thermodynamicscited by 7,005
  10. 2018A Style-Based Generator Architecture for Generative Adversarial Networkscited by 5,986
  11. 2017Progressive Growing of GANs for Improved Quality, Stability, and Variationcited by 2,967
  12. 2020Fourier Neural Operator for Parametric Partial Differential Equationscited by 2,806
24 more
  1. 2018Large Scale GAN Training for High Fidelity Natural Image Synthesiscited by 2,609
  2. 2019DeepONet: Learning nonlinear operators for identifying differential equations based on the universal approximation theorem of operatorscited by 1,965
  3. 2016Photo-Realistic Single Image Super-Resolution Using a Generative Adversarial Networkcited by 1,898
  4. 2021SwinIR: Image Restoration Using Swin Transformercited by 1,869
  5. 2017Enhanced Deep Residual Networks for Single Image Super-Resolutioncited by 1,543
  6. 2021Image Super-Resolution via Iterative Refinementcited by 1,506
  7. 2018ESRGAN: Enhanced Super-Resolution Generative Adversarial Networkscited by 1,250
  8. 2014Image Super-Resolution Using Deep Convolutional Networkscited by 1,218
  9. 2015Accurate Image Super-Resolution Using Very Deep Convolutional Networkscited by 902
  10. 2020Pre-Trained Image Processing Transformercited by 891
  11. 2018Residual Dense Network for Image Super-Resolutioncited by 796
  12. 2020NVAE: A Deep Hierarchical Variational Autoencodercited by 635
  13. 2020Learning Continuous Image Representation with Local Implicit Image Functioncited by 550
  14. 2021Neural Operator: Learning Maps Between Function Spacescited by 413
  15. 2016Accelerating the Super-Resolution Convolutional Neural Networkcited by 393
  16. 2016AID: A Benchmark Dataset for Performance Evaluation of Aerial Scene Classificationcited by 343
  17. 2021Choose a Transformer: Fourier or Galerkincited by 333
  18. 2015Deeply-Recursive Convolutional Network for Image Super-Resolutioncited by 317
  19. 2021Multiwavelet-based Operator Learning for Differential Equationscited by 229
  20. 2023Super-Resolution Neural Operatorcited by 48
  21. 2021Solving PDE-constrained Control Problems Using Operator Learningcited by 41
  22. 2024DRCT: Saving Image Super-resolution away from Information Bottleneckcited by 40
  23. 2021A Comprehensive Review of Deep Learning-based Single Image Super-resolutioncited by 23
  24. 2020CDGAN: Cyclic Discriminative Generative Adversarial Networks for Image-to-Image Transformationcited by 3

Built on by

Cited by year (2026 to 7 Oct)

Citing papers per year: 2025: 1; 2026: 2.

3 papers.

  1. 2025Frequency-Aware Vision Transformers for High-Fidelity Super-Resolution of Earth System Modelscited by 1
  2. 2026Beyond Visual Fidelity: Benchmarking Super-Resolution Models for Large-Scale Remote Sensing Imagery via Downstream Task Integrationcited by 1
  3. 2026Super-Resolving Unseen Hyperspectral Sensors at Any Scale via Spatial Operatorscited by 0

Similar, not cited

Close in wording, no citation either way

  1. 2024Latent Diffusion, Implicit Amplification: Efficient Continuous-Scale Super-Resolution for Remote Sensing Images
  2. 2026SlimDiffSR: Toward Lightweight and Efficient Remote Sensing Image Super-Resolution via Diffusion Model Distillation
  3. 2025Single-Step Latent Consistency Model for Remote Sensing Image Super-Resolution
  4. 2026Learning to Balance: Decoupled Siamese Diffusion Transformer for Reference-Based Remote Sensing Image Super-Resolution
  5. 2025TTRD3: Texture Transfer Residual Denoising Dual Diffusion Model for Remote Sensing Image Super-Resolution
  6. 2026InfScene-SR: Seamless Super-Resolution of Arbitrarily Large Remote-Sensing Scenes via Variance-Preserving Joint Denoising
  7. 2024Adaptive Semantic-Enhanced Denoising Diffusion Probabilistic Model for Remote Sensing Image Super-Resolution
  8. 2025Controllable Reference Guided Diffusion with Local Global Fusion for Real World Remote Sensing Image Super Resolution