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Acquisition super resolution from infrared images using proposed techniques

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Abstract

This paper suggests three novel proposed techniques for super resolution (SR) infrared (IR) images. The first algorithm is relied on the image acquisition model, which considers benefits of the sparse representations of low resolution (LR) and high resolution (HR) patches using Bi-cubic interpolation and minimum mean square error (MMSE) estimation. This estimation in HR image prediction stage providing a scheme can be interpreted as a feed forward neural network. The second scheme is based on up-sampling for IR images using Second Kernel Lanczos Interpolation (SKLI).The third scheme is depended on up-sampling for IR images using Third Kernel Lanczos Interpolation (TKLI).This technique is typically used to increase the sampling rate of a digital signal, or to shift it by a fraction of the sampling interval.

The performance metrics are Peak Signal-To-Noise Ratio (PSNR) and computation time. Simulation results prove that the success of three presented techniques in acquisition high resolution of SR IR images. By comparing the three presented algorithms with Regularized Interpolation (REI) and least squares Interpolation (LSI) schemes of IR images. It is clear that the second suggested technique gives superior than REI and LSI schemes from point views PSNR and computation time. On the other hand the third presented technique is the best algorithms from point views PSNR and computation time to other techniques.

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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

  2. Ashiba HI (2020) "Feature Enhancement Angiographic Images In Medical Diagnosis", Multimed Tools Appl

  3. Ashiba HI (2020) “Cepstrum adaptive plateau histogram for dark IR night vision images enhancement”, springer. Multimed Tools Appl 79:2543–2554

    Article  Google Scholar 

  4. Ashiba HI, Awadalla KH, El-Halfawy SM, Abd El-Samie FE (2011) Adaptive least squares interpolation of infrared images. Springer. J Circuits, Sys Signal Process 30:543–551

    Article  MATH  Google Scholar 

  5. Ashiba HI, Mansour HM, Ahmed HM, El-Kordy MF, Dessouky MI, El-Samie FEA (2018) Enhancement of infrared images based on efficient histogram processing. Wireless Pers Commun 99:619–636

    Article  Google Scholar 

  6. Ashiba HI, Mansour HM, Ahmed HM, El-Kordy MF, Dessouky MI, Zahran O, El-Samie FEA (2019) Enhancement of IR images using histogram processing and the Undecimated additive wavelet transform. Multimed Tools Appl 78(9):11277–11290

    Article  Google Scholar 

  7. Ashiba MI, Ashiba HI, Tolba MS, El-Fishawy AS, El-Samie FEA (2020) An efficient proposed framework for infrared night vision imaging system. Multimed Tools Appl 79:23111–23146. https://doi.org/10.1007/s11042-020-09039-6

  8. Bahy RM, Salama GI, Tarek A (2014) Mahmoud, Adaptive regularization based super resolution reconstruction technique for multi-focus low resolution images. Signal Process:155–167. https://doi.org/10.1016/j.sigpro.2014.01.008

  9. Chen T, Wu HR, Qiu B (2001) Image interpolation using across-scalepixel correlation, IEEE International Conference: Acoustics, Speech, and Signal Processing (ICAASP '01) Proceedings 3:1857–1860. https://doi.org/10.1109/ICASSP.2001.941305

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

  11. 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. https://doi.org/10.1590/S0104-65002006000100006

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

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

    Article  MATH  Google Scholar 

  14. Han JK, Kim HM (2001) Modified cubic convolution scaler with minimum loss of information. Opt Eng 40(4):540–546

    Article  MathSciNet  Google Scholar 

  15. Hou HS, Andrews HC (1978) Cubic spline for image interpolation and digital filtering, IEEE trans. Acoustics. Speech Signal Process ASSP-26(9):508–517

    MATH  Google Scholar 

  16. 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

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

    Article  MathSciNet  MATH  Google Scholar 

  18. Mallat S, Yu G (2010) Super-resolution with sparse mixing estimators. IEEE Transactions on Image Processing 19(11):2889–2900. https://doi.org/10.1109/TIP.2010.2049927

  19. 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 

  20. Peleg T, Michael Elad A (2014) A statistical prediction model based on sparse representations for single image super-resolution. IEEE Trans Image Process 23:2569–2582. https://doi.org/10.1109/TIP.2014.2305844

  21. Shin JH, Jung JH, Paik JK (1998) Regularized iterative image interpolation and its application to spatially scalable coding, IEEE trans. Consum Electron 44(3):1042–1047

    Article  Google Scholar 

  22. Sun J, Zhu J, Tappen M. F (2010) Context-constrained hallucination for image super-resolution, in proc. IEEE Conf Comput Vision and Pattern Recognition, 1-8

  23. Thevenaz P, Blu T, Unser M (2000) Interpolation revisited. IEEE Trans Medical Imaging 19:739–758

    Article  Google Scholar 

  24. Tian J, Ma KK (2010) Stochastic super-resolution image reconstruction. J Vis Commun Image Represent 21:232–244

    Article  Google Scholar 

  25. 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

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

    Article  MathSciNet  MATH  Google Scholar 

  27. 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 

  28. 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  MATH  Google Scholar 

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

    Article  Google Scholar 

  30. Zeyde R, Elad Protter M (2012) On single image scale-up using sparse-representations, International Conference on Curves Surfaces, 711–730

  31. 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  MATH  Google Scholar 

  32. 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  MATH  Google Scholar 

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

    Article  Google Scholar 

  34. Zhao Y, Sui X, Chen Q, Wu S (2016) Learning-based compressed sensing for infrared image super resolution. Infrared Phys Technol 76:139–147. https://doi.org/10.1016/j.infrared.2016.02.001

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

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  1. Department of Electronics and Electrical Communications Engineering, Bilbis Higher Institute of Engineering, Sharqia, Bilbis, Egypt

    H. I. Ashiba

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Ashiba, H.I. Acquisition super resolution from infrared images using proposed techniques. Multimed Tools Appl 82, 2329–2348 (2023). https://doi.org/10.1007/s11042-022-13273-5

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