SpringerLink
Log in

Infrared image super-resolution reconstruction based on residual fast fourier transform

  • Published:
Multimedia Tools and Applications Aims and scope Submit manuscript

Abstract

Infrared images have been widely used in military, civilian, and industrial fields. Due to the inherent limitations of sensors, infrared images usually have some disadvantages such as low resolution and blurred texture details. How to improve the resolution of infrared images without changing hardware devices has become a current research hotspot. Aiming at the problem that the existing infrared image super-resolution reconstruction methods do not utilize the image details sufficiently, this paper proposes an infrared image super-resolution reconstruction algorithm based on residual fast fourier transform(ISRRFT). The image features are extracted in both spatial domain and frequency domain, so as to fully extract the high-frequency and low-frequency information components of infrared images and improve the quality of reconstructed infrared images In addition, the learnable fast fourier transform loss function has been introduced, which is used in conjunction with the L1 loss function to calculate the loss from both the spatial and frequency domains, thus better optimizing the model parameters. The test results on three test sets, SR1280, IRData and Infrared20, show that the proposed algorithm has an optimal performance in terms of both objective and subjective evaluation metrics compared to current representative algorithms.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Log in via an institution

Subscribe and save

Springer+ Basic
EUR 32.99 /Month
  • Get 10 units per month
  • Download Article/Chapter or Ebook
  • 1 Unit = 1 Article or 1 Chapter
  • Cancel anytime
Subscribe now

Buy Now

Price includes VAT (Germany)

Instant access to the full article PDF.

Institutional subscriptions

Fig. 1
Fig. 2
Fig. 3
Fig.4
Fig. 5
Fig. 6
Fig. 7
Fig. 8
Fig. 9
Fig. 10
Fig. 11
Fig. 12
Fig. 13

Similar content being viewed by others

References 

  1. Billon-Lanfrey D, Trinolet P, Pistone F et al (2010) New IR detectors with small pixel pitch and high operating temperature[C]// Infrared, Millimeter Wave, and Terahertz Technologies. SPIE 7854:128–138

    Google Scholar 

  2. Baker S, Kanade T (2002) Limits on super-resolution and how to break them[J]. IEEE Trans Pattern Anal Mach Intell 24(9):1167–1183

    Article  Google Scholar 

  3. Li Yun（2010) Research and implementation of super resolution algorithm for video sequences [D]. Changsha: Central South University

  4. Park SC, Park MK, Kang MG (2003) Super-resolution image reconstruction: a technical overview[J]. IEEE Signal Process Mag 20(3):21–36

    Article  Google Scholar 

  5. Tasi RY, Huang TS (1984) Multipleframe image restoration and registration[A]. Advances in Computer Vision ang Image Processing Greenwich 317–339

  6. Kim SP, Bose NK, Valenzuela HM (1990) Recursive reconstruction of high resolution image from noisy undersampled multiframes[J]. IEEE Trans Acoust Speech Signal Proc 38(6):1013–1027

    Article  Google Scholar 

  7. Kim SP, Su WY (1993) Recursive high-resolution reconstruction of blurred multiframe images[J]. IEEE Trans Image Process 2(4):534–539

    Article  Google Scholar 

  8. Rhee S, Kang MG (1999) Discrete cosine transform based regularized high-resolution image reconstruction algorithm[J]. Opt Eng 38(8):1348–1356

    Article  Google Scholar 

  9. Li X, Orchard MT (2001) New edge-directed interpolation[J]. IEEE Trans Image Proc 10(10):1521–1527

    Article  Google Scholar 

  10. Zhang L, Wu X (2006) An edge-guided image interpolation algorithm via directional filtering and data fusion. IEEE Trans Image Process 15(8):2226–2238

    Article  Google Scholar 

  11. Stark H, Oskoui P (1989) High-resolution image recovery from image-plane arrays, using convex projections[J]. J Opt Soc Am A 6(11):1715–1726

    Article  Google Scholar 

  12. Irani M, Peleg S (1991) Improving resolution by image registration[J]. CVGIP: Graphical Models Image Proc 53(3):231–239

    Google Scholar 

  13. Schultz RR, Stevenson RL (1996) Extraction of high-resolution frames from video sequences[J]. IEEE Trans Image Process 5(6):996–1011

    Article  Google Scholar 

  14. Tai YW, Liu S, Brown MS et al (2010) Super resolution using edge prior and single image detail synthesis[C]. 2010 IEEE computer society conference on computer vision and pattern recognition 2400–2407

  15. Zhang K, Gao X, Tao D et al (2012) Single image super-resolution with non-local means and steering kernel regression[J]. IEEE Trans Image Process 21(11):4544–4556

    Article  MathSciNet  Google Scholar 

  16. Dong C, Loy CC, He K et al (2014) Learning a deep convolutional network for image super-resolution[C]. European conference on computer vision 184–199

  17. Kim J, Lee JK, Lee KM (2016) Accurate image super-resolution using very deep convolutional networks[C]. Proceedings of the IEEE conference on computer vision and pattern recognition 1646–1654

  18. Ledig C, Theis L, Huszár F et al (2017) Photo-realistic single image super-resolution using a generative adversarial network[C]. Proceedings of the IEEE conference on computer vision and pattern recognition 4681–4690

  19. Lim B, Son S, Kim H et al (2017) Enhanced deep residual networks for single image super-resolution[C]. Proceedings of the IEEE conference on computer vision and pattern recognition workshops 136–144

  20. Haris M, Shakhnarovich G, Ukita N (2018) Deep back-projection networks for super-resolution[C]. Proceedings of the IEEE conference on computer vision and pattern recognition 1664–1673

  21. Zhang Y, Tian Y, Kong Y et al (2018) Residual dense network for image super-resolution[C]. Proceedings of the IEEE conference on computer vision and pattern recognition 2472–2481

  22. Chen L, Tang R, Anisetti M et al (2021) A lightweight iterative error reconstruction network for infrared image super-resolution in smart grid[J]. Sustain Cities Soc 66:102520

    Article  Google Scholar 

  23. Zhu S, Zhou C, Wang Y (2022) Super resolution reconstruction method for infrared images based on pseudo transferred features[J]. Displays 74:102187

    Article  Google Scholar 

  24. He K, Zhang X, Ren S et al (2015) Delving deep into rectifiers: surpassing human-level performance on imagenet classification[C]. Proceedings of the IEEE international conference on computer vision 1026–1034

  25. Shi W, Caballero J, Huszár F et al (2016) Real-time single image and video super-resolution using an efficient sub-pixel convolutional neural network[C]. Proceedings of the IEEE conference on computer vision and pattern recognition 1874–1883

  26. SR1280. Available online http://openai.raytrontek.com/E_new/. Accessed 8 Apr 2022

  27. Project, D.G.IR Data available online http://www.dgp.toronto.edu/~nmorris/data/IRData/. Accessed 23 Nov 2019

  28. He Z, Tang S, Yang J et al (2018) Cascaded deep networks with multiple receptive fields for infrared image super-resolution[J]. IEEE Trans Circuits Syst Video Technol 29(8):2310–2322

    Article  Google Scholar 

  29. Keys R (1981) Cubic convolution interpolation for digital image processing[J]. IEEE Trans Acoust Speech Signal Process 29(6):1153–1160

    Article  MathSciNet  Google Scholar 

  30. Zhang Y, Li K, Li K et al (2018) Image super-resolution using very deep residual channel attention networks[C]. Proceedings of the European conference on computer vision (ECCV) 286–301

  31. Wang C, Ning X, Sun L et al (2022) Learning discriminative features by covering local geometric space for point cloud analysis[J]. IEEE Trans Geosci Remote Sens 60:5703215

    Google Scholar 

  32. Ning X, Tian W, He F et al (2023) Hyper-sausage coverage function neuron model and learning algorithm for image classification[J]. Pattern Recogn 136:109216

    Article  Google Scholar 

Download references

Acknowledgements

This work was supported by the Key Scientific and Technological Support Projects of Tian** Key R&D Program [18YFZCGX00930]. Thanks to Yantai IRay Technology Co., Ltd. for the free open source infrared data set. The authors also acknowledge the anonymous reviewers for their helpful comments on the manuscript.

Author information

Authors and Affiliations

  1. School of Electronics and Information Engineering, Tiangong University, Tian**, China

    **anguo Li, Rui Liu & Yanli Yang

  2. Tian** Key Laboratory of Optoelectronic Detection Technology and System, Tian**, China

    **anguo Li & Yanli Yang

Authors
  1. **anguo Li
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Rui Liu
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Yanli Yang
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to **anguo Li.

Ethics declarations

Conflict Of interest

The authors declare that there is no conflict of interest.

Additional information

Publisher's Note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Rights and permissions

Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law.

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Li, X., Liu, R. & Yang, Y. Infrared image super-resolution reconstruction based on residual fast fourier transform. Multimed Tools Appl (2024). https://doi.org/10.1007/s11042-024-19236-2

Download citation

  • Received:

  • Revised:

  • Accepted:

  • Published:

  • DOI: https://doi.org/10.1007/s11042-024-19236-2

Keywords

Search

Navigation