Skip to main content
Log in

Enhancement of Infrared Images Using Super Resolution Techniques Based on Big Data Processing

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

Abstract

This paper presents a super-resolution (SR) technique for enhancement of infrared (IR) images. The suggested technique relies on the image acquisition model, which benefits from the sparse representations of low-resolution (LR) and high-resolution (HR) patches of the IR images. It uses bicubic interpolation and minimum mean square error (MMSE) estimation in the prediction of the HR image with a scheme that can be interpreted as a feed-forward neural network. The suggested algorithm to overcome the problem of having only LR images due to hardware limitations is represented with a big data processing model. The performance of the suggested technique is compared with that of the standard regularized image interpolation technique as well as an adaptive block-by-block least-squares (LS) interpolation technique from the peak signal-to-noise ratio (PSNR) perspective. Numerical results reveal the superiority of the proposed SR technique.

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

Access this article

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5
Fig. 6

Similar content being viewed by others

References

  1. Aharon M, Elad M, Bruckstein AM (2006) K-SVD: an algorithm for designing overcomplete dictionaries for sparse representation. IEEE Trans Signal Processing 54(11):4311–4322

    Article  Google Scholar 

  2. Armstrong GR, Packard PD 1996 CMT and PtSi FLIR systems for EUCLID RTP 8.1, in: Proc. SPIE, 257–266

  3. Ashiba HI, Awadalla KH, El-Halfawy SM, Abd El-Samie FE (2011) Adaptive Least Squares Interpolation of Infrared Images. Springer, Journal of Circuits, Systems and Signal Processing 30:543–551

    Article  MathSciNet  Google Scholar 

  4. Bahy RM, Salama GI, Mahmoud TA (2014) Adaptive regularization based super resolution reconstruction technique for multi-focus low resolution images, Signal Process. 155–167

    Article  Google Scholar 

  5. Baker S, Kannade T (2002) Limits on super resolution and how to break them. IEEE Trans Pattern Anal Mach Intell 24(9):1167–1183

    Article  Google Scholar 

  6. Chen T, Wu HR, Qiu B (2001) Image Interpolation Using Across-Scale Pixel Correlation, Proceedings of ICASSP

  7. Donoho DL (2006) Compressed sensing. IEEE Transactions on InformationTheory 52:1289–1306

    Article  MathSciNet  Google Scholar 

  8. El-Khamy SE, Hadhoud MM, Dessouky MI, Salam BM, Abd El-Samie FE (2006) A new approach for regularized image interpolation. J Braz Comput Soc 11(3):65–79

    Article  Google Scholar 

  9. Fattal R (2007) Image upsampling via imposed edge statistics, ACM Transactions on Graphics (TOG), vol. 26(3), ACM

  10. Fortin J, Chevrette P (1996) Realization of a fast micro-scanning device for infrared focal plane arrays, in: Proc, SPIE 2743, pp. 185

  11. Freeman WT, Pasztor EC, Carmichael OT (2000) Learning low-level vision. Int JComput Vis 40(1):25–47

    Article  Google Scholar 

  12. Han JK, Kim HM (2001) Modified Cubic Convolution Scaler with Minimum Loss of Information. Opt Eng 40(4):540–546

    Article  Google Scholar 

  13. Hou HS, Andrews HC (1978) Cubic Spline For Image Interpolation and Digital Filtering, IEEE Trans. Acoustics , Speech and Signal Processing, vol. ASSP-26 ,9:508–517

  14. Huang J, Singh A, Ahuja N (2015) Single Image Super-resolution from Transformed Self-Exemplars. IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pp. 5197–5206

  15. Keys R (1981) Cubic convolution interpolation for digital image processing. Acoustics, Speech and Signal Processing, IEEE Transactions on 29(6):1153–1160

    Article  MathSciNet  Google Scholar 

  16. Liu Y , Nie L, Liu L, Rosenblum DS (2015) From action to activity: Sensor-based activity recognition. Neurocomputing

  17. Liu Y, Nie L, Han L, Zhang L, Rosenblum DS (2015) Action2Activity: Recognizing Complex Activities from Sensor Data. IJCAI ,PP.1617–1623

  18. Liu Y, Zhang L, Nie L, Yan Y, Rosenblum DS (2016) Fortune Teller: Predicting Your Career Path The 30th AAAI Conference on Artificial Intelligence, PP.201–207

  19. Mallat S, Yu G (2010) Super-resolution with sparse mixing estimators. IEEE Trans Image Process 19(11):2889–2900

    Article  MathSciNet  Google Scholar 

  20. Mao Y, Wang Y, Zhou J, Jia H (2016) An infrared image super-resolution reconstruction method based on compressive sensing. Infrared Phys Technol 76:735–739

    Article  Google Scholar 

  21. Peleg T, Elad M (2014) A Statistical Prediction Model Based on Sparse Representations for Single Image Super-Resolution. IEEE Trans Image Process 23:2569–2582

    Article  MathSciNet  Google Scholar 

  22. Shin JH, Jung JH, Paik JK (1998) Regularized Iterative Image Interpolation And Its Application To Spatially Scalable Coding. IEEE Trans Consumer Electronics 44(3):1042–1047 August

    Article  Google Scholar 

  23. Sun J, Zhu J, Tappen MF (2010) Context-constrained hallucination for image super-resolution, in Proc. IEEE Conf. Comput. Vision and Pattern Recognition, 1–8

  24. Thevenaz P, Blu T, Unser M (2000) Interpolation Revisited, IEEE Trans. Medical Imaging, vol.19, 739–758

  25. Tian J, Ma KK (2010) Stochastic super-resolution image reconstruction, J. Vis. Commun. Image Represent. 232–244

    Article  Google Scholar 

  26. Unser M (1999) Splines A Perfect Fit For Signal and Image Processing, IEEE Signal Processing Magazine

  27. Ur H, Gross D (1992) Improved resolution from sub-pixel shifted pictures, CVGIP: Graph. Models Image Process. 54: 181–186

  28. Wang Z, Liu D, Yang J, Han W, Huang T (2015) Deep Networks for Image Super-Resolution with Sparse Prior, IEEE International Conference on Computer Vision (ICCV), 370–378

  29. Yang J, Wright J, Huang T, Ma Y (2010) Image super-resolution via sparse representation. IEEE Trans Image Process 19:2861–2873

    Article  MathSciNet  Google Scholar 

  30. Yang X, Wu W, Liu K, Zhou K, Yan B (2016) Fast multisensor infrared image super-resolution scheme with multiple regression models. J Syst Archit 64:11–25

    Article  Google Scholar 

  31. Yu G, Sapiro G, Mallat S (2012) Solving inverse problems with piecewise linear estimators: from Gaussian mixture models to structured sparsity. IEEE Trans Image Processing 21(5):2481–2499

    Article  MathSciNet  Google Scholar 

  32. Yue L, Shen H, Li J, Yuan Q, Zhang H, Zhang L (2016) Image super-resolution: The techniques, applications, and future, Signal Processing. 128: 389–408

    Article  Google Scholar 

  33. Zeyde R, Elad M, Protter, 2012 single image scale-up using Sparse-representations, Curves and Surfaces, 711–730

  34. Zhang H, Zhang Y, Li H (2012) Generative Bayesian image super resolution with natural image prior. IEEE Trans Image Process 21(9):4054–4067

    Article  MathSciNet  Google Scholar 

  35. Zhang K, Tao D, Gao X, Li X, Xiong Z (2015) Learning multiple linear mappings for efficient single image super-resolution. IEEE Trans Image Process 24:846–861

    Article  MathSciNet  Google Scholar 

  36. Zhao Y, Chen Q, Sui X, Gu G (2015) A novel infrared image super-resolution method based on sparse representation. Infrared Phys Technol 71:506–513

    Article  Google Scholar 

  37. Zhao Y, Sui X, Chen Q, Wu S (2016) Learning-based compressed sensing for infrared image super resolution. Infrared Phys Technol 76:139–147

    Article  Google Scholar 

  38. Zhu Y, Zhang Y, Yuille AL (2014) Single Image Super-resolution using Deformable Patches , Proc.IEEE Conf. Comput. Vision and Pattern Recognition, 1–8

Download references

Author information

Authors and Affiliations

Authors

Corresponding authors

Correspondence to Fathi E. Abd El-Samie or Huda I. Ashiba.

Additional information

Publisher’s note

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

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Abd El-Samie, F.E., Ashiba, H.I., Shendy, H. et al. Enhancement of Infrared Images Using Super Resolution Techniques Based on Big Data Processing. Multimed Tools Appl 79, 5671–5692 (2020). https://doi.org/10.1007/s11042-019-7634-0

Download citation

  • Received:

  • Revised:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s11042-019-7634-0

Keywords

Navigation