mlsanityData to 7 Oct 2026

Infrared Image Super-Resolution: Systematic Review, and Future Trends

arXiv 2212.12322 ↗First posted 22 Dec 2022eess.IV cs.CV cs.LGcited by 10

Not on the map. Why?

Abstract (arXiv)

Image Super-Resolution (SR) is essential for a wide range of computer vision and image processing tasks. Investigating infrared (IR) image (or thermal images) super-resolution is a continuing concern within the development of deep learning. This survey aims to provide a comprehensive perspective of IR image super-resolution, including its applications, hardware imaging system dilemmas, and taxonomy of image processing methodologies. In addition, the datasets and evaluation metrics in IR image super-resolution tasks are also discussed. Furthermore, the deficiencies in current technologies and possible promising directions for the community to explore are highlighted. To cope with the rapid development in this field, we intend to regularly update the relevant excellent work at https://github.com/yongsongH/Infrared_Image_SR_Survey.

Key facts. Cited by 10 papers in the map (data through 7 Oct 2026). Builds on 51 of its 262 references that are in the map. First posted on arXiv 22 Dec 2022.

Builds on

51 of 262 references.

References · 51

  1. 2021Learning Transferable Visual Models From Natural Language Supervisioncited by 33,456
  2. 2021High-Resolution Image Synthesis with Latent Diffusion Modelscited by 18,694
  3. 2021LoRA: Low-Rank Adaptation of Large Language Modelscited by 14,084
  4. 2021Swin Transformer: Hierarchical Vision Transformer using Shifted Windowscited by 12,082
  5. 2018The Unreasonable Effectiveness of Deep Features as a Perceptual Metriccited by 11,029
  6. 2022Generative Adversarial Networkscited by 5,603
  7. 2017Improved Training of Wasserstein GANscited by 2,223
  8. 2016Photo-Realistic Single Image Super-Resolution Using a Generative Adversarial Networkcited by 1,898
  9. 2021SwinIR: Image Restoration Using Swin Transformercited by 1,869
  10. 2017Enhanced Deep Residual Networks for Single Image Super-Resolutioncited by 1,543
  11. 2022CoCa: Contrastive Captioners are Image-Text Foundation Modelscited by 1,265
  12. 2018Image Super-Resolution Using Very Deep Residual Channel Attention Networkscited by 1,256
38 more
  1. 2018ESRGAN: Enhanced Super-Resolution Generative Adversarial Networkscited by 1,250
  2. 2014Image Super-Resolution Using Deep Convolutional Networkscited by 1,218
  3. 2021MUSIQ: Multi-scale Image Quality Transformercited by 1,058
  4. 2021Transformers in Vision: A Surveycited by 1,009
  5. 2021Real-ESRGAN: Training Real-World Blind Super-Resolution with Pure Synthetic Datacited by 961
  6. 2015Accurate Image Super-Resolution Using Very Deep Convolutional Networkscited by 902
  7. 2021Transformer in Transformercited by 632
  8. 2021Designing a Practical Degradation Model for Deep Blind Image Super-Resolutioncited by 524
  9. 2022Activating More Pixels in Image Super-Resolution Transformercited by 433
  10. 2016Accelerating the Super-Resolution Convolutional Neural Networkcited by 393
  11. 2019Deep Learning for Image Super-resolution: A Surveycited by 368
  12. 2022Target-aware Dual Adversarial Learning and a Multi-scenario Multi-Modality Benchmark to Fuse Infrared and Visible for Object Detectioncited by 313
  13. 2022A Survey on Generative Diffusion Modelcited by 238
  14. 2022Focal Modulation Networkscited by 186
  15. 2019Feedback Network for Image Super-Resolutioncited by 155
  16. 2020Structure-Preserving Super Resolution with Gradient Guidancecited by 116
  17. 2021ViTGAN: Training GANs with Vision Transformerscited by 110
  18. 2022Accelerating Diffusion Models via Early Stop of the Diffusion Processcited by 100
  19. 2021Blind Image Super-Resolution: A Survey and Beyondcited by 93
  20. 2022Contrastive Learning Rivals Masked Image Modeling in Fine-tuning via Feature Distillationcited by 93
  21. 2021Real-World Single Image Super-Resolution: A Brief Reviewcited by 65
  22. 2021Deep Learning for HDR Imaging: State-of-the-Art and Future Trendscited by 45
  23. 2022ShuffleMixer: An Efficient ConvNet for Image Super-Resolutioncited by 44
  24. 2021Pyramid Adversarial Training Improves ViT Performancecited by 39
  25. 2022Learning the Degradation Distribution for Blind Image Super-Resolutioncited by 27
  26. 2022A Closer Look at Blind Super-Resolution: Degradation Models, Baselines, and Performance Upper Boundscited by 22
  27. 2022From Face to Natural Image: Learning Real Degradation for Blind Image Super-Resolutioncited by 15
  28. 2021Toward Real-World Super-Resolution via Adaptive Downsampling Modelscited by 14
  29. 2020Multimodal Deep Unfolding for Guided Image Super-Resolutioncited by 13
  30. 2017Multimodal Image Super-resolution via Joint Sparse Representations induced by Coupled Dictionariescited by 8
  31. 2018Multimodal Sensor Fusion In Single Thermal image Super-Resolutioncited by 8
  32. 2019Vine disease detection in UAV multispectral images with deep learning segmentation approachcited by 8
  33. 2022Joint Learning Content and Degradation Aware Feature for Blind Super-Resolutioncited by 8
  34. 2022A Survey on Infrared Image and Video Setscited by 7
  35. 2022Degradation-Guided Meta-Restoration Network for Blind Super-Resolutioncited by 7
  36. 2021Infrared Image Super-Resolution via Heterogeneous Convolutional WGANcited by 6
  37. 2022Learning Multiple Probabilistic Degradation Generators for Unsupervised Real World Image Super Resolutioncited by 6
  38. 2022Rethinking Degradation: Radiograph Super-Resolution via AID-SRGANcited by 4

50 most cited of 51 All 51 (JSON)

Built on by

Cited by year (2026 to 7 Oct)

Citing papers per year: 2023: 3; 2024: 2; 2025: 1; 2026: 4.

10 papers.