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A new wavelet-based multi-focus image fusion technique using method noise and anisotropic diffusion for real-time surveillance application

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Abstract

This paper presents a new wavelet-based multi-focus image fusion approach using method noise and anisotropic diffusion for two separate cases, i.e., with and without a reference image. It is specifically designed for real-time surveillance applications. It is a multi-step image fusion approach. Firstly, stationary wavelet transform (SWT) is performed to get low and high-frequency coefficients. Secondly, the input images' LL bands are fused using average operation. The rest of the respective bands are fused using a new correlation coefficient (CC) based fusion strategy using the threshold value calculated by structural similarity index metric (SSIM). Then inverse SWT is performed to reconstruct the fused coefficients. Thirdly, anisotropic diffusion-based method noise thresholding is introduced to recover the unprocessed and still damaged input images' components. Finally, the proposed approach's performance has experimented with various qualitative (visual perception) and quantitative factors (performance metrics). The experimental outcomes show that the proposed approach generates fine edges, high visual quality, high clarity of objects, and less degradation. The proposed multi-step hybrid technique is implemented to generate high-quality fused images. The experimental outcomes verify the achievement of the proposed approach.

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References

  1. Ma, J., Ma, Y., Li, C.: Infrared and visible image fusion methods and applications: a survey. Inform. Fusion 45, 153–178 (2019)

    Article  Google Scholar 

  2. Singh, P., Diwakar, M., Shankar, A., Shree, R., Kumar, M.: A review on SAR image and its despeckling. Arch. Comput. Methods Eng. 187, 1–21 (2021)

    Google Scholar 

  3. Singh, P., Shankar, A.: A novel optical image denoising technique using convolutional neural network and anisotropic diffusion for real-time surveillance applications. J. Real-Time Image Process. 108, 1–18 (2021)

    Google Scholar 

  4. Singh, P. K., Ashok, A.: A new multi-focus image fusion technique for an efficient surveillance. In: 2019 4th International conference on internet of things: smart innovation and usages (IoT-SIU), Ghaziabad, India, 2019, pp 1–6, https://doi.org/10.1109/IoT-SIU.2019.8777593

  5. Singh, P., et al.: A new SAR image despeckling using correlation based fusion and method noise thresholding. J. King Saud Univ. Comp. Inform. Sci. (2018). https://doi.org/10.1016/j.jksuci.2018.03.009

    Article  Google Scholar 

  6. Bhatt, M. B., Arya, D., Mishra, A. N., Singh, M., Singh, P., Gautam, M.: A new wavelet-based multifocus image fusion technique using method noise-median filtering. In: 2019 4th International Conference on Internet of Things: Smart Innovation and Usages (IoT-SIU), Ghaziabad, India, 2019, pp. 1–6, https://doi.org/10.1109/IoT-SIU.2019.8777615

  7. Singh, P., Diwakar, M.: Wavelet-based multi-focus image fusion using average method noise diffusion (AMND). Recent Adv. Comp. Sci. Commun. 14, 2422 (2021). https://doi.org/10.2174/2666255813999200720163938

    Article  Google Scholar 

  8. Liu, C., Long, Y., Mao, J.: Energy efficient multi-focus image fusion based on neighbour distance and morphology. Optik 127, 11354–11363 (2016)

    Article  Google Scholar 

  9. Burt, P., Adelson, E.: The laplacian pyramid as a compact image code. IEEE Trans. Comput. 31(4), 532–540 (1983)

    Google Scholar 

  10. Petrovic, V.S., Xydeas, C.S.: Gradient-based multiresolution image fusion. IEEE Trans. Image Process. 13(2), 228–237 (2004)

    Article  Google Scholar 

  11. Liu, Z., Tsukada, K., Hanasaki, K., Ho, Y., Dai, Y.: Image fusion by using steerable pyramid. Pattern Recognit. Lett. 22(9), 929–939 (2001)

    Article  Google Scholar 

  12. Amolins, K., Zhang, Y., Dare, P.: Wavelet based image fusion techniques an introduction, review and comparison. ISPRS J. Photogramm. Remote Sens 62(4), 249–263 (2007)

    Article  Google Scholar 

  13. Li, H., Manjunath, B., Mitra, S.: Multisensor image fusion using the wavelet transform. Graph. Model Image Process. 57(3), 235–245 (1995)

    Article  Google Scholar 

  14. Tian, J., Chen, L.: Adaptive multi-focus image fusion using a wavelet-based statistical sharpness measure. Signal Process. 92(9), 2137–2146 (2012)

    Article  Google Scholar 

  15. Redondo, R., Šroubek, F., Fischer, S., Cristóbal, G.: Multifocus image fusion using the log-gabor transform and a multisize windows technique. Inf. Fusion 10(2), 163–171 (2009)

    Article  Google Scholar 

  16. Pajares, G., de la Cruz, J.M.: A wavelet-based image fusion tutorial. Pattern Recogn. 37(9), 1855–1872 (2004)

    Article  Google Scholar 

  17. Naidu, V. P. S.: Multi-resolution image fusion by FFT. In 2011 International Conference on Image Information Processing. IEEE, pp. 1–6 (2011)

  18. Tessens, L., Ledda, A., Pizurica, A., Philips, W.: Extending the depth of field in microscopy through curvelet-based frequency-adaptive image fusion. In: IEEE Int. Conf. on Acoustics, Speech and Signal Processing (ICASSP), 1 (2007), pp. I-861–I-864

  19. Li, S., Yang, B.: Multifocus image fusion by combining curvelet and wavelet transform. Pattern Recogn. Lett. 29(9), 1295–1301 (2008)

    Article  Google Scholar 

  20. Candes, E., Demanet, L., Donoho, D., Ying, L.: Fast discrete curvelet transforms. Multiscale Model. Simul. 5(3), 861–899 (2006)

    Article  MathSciNet  Google Scholar 

  21. Zhang, Q., Guo, B.L.: Multifocus image fusion using the non-sub sampled contourlet transform. Signal Process. 89(7), 1334–1346 (2009)

    Article  Google Scholar 

  22. Yang, L., Guo, B., Ni, W.: Multimodality medical image fusion based on multiscale geometric analysis of contourlet transform. Neurocomputing 72(1–3), 203–211 (2008)

    Article  Google Scholar 

  23. Qiguang, M., Baoshu, W.: A novel image fusion method using contourlet transform. Int. Conf. Commun. Circuits Syst 1, 548–552 (2006)

    Google Scholar 

  24. Samet, A., Cemal, K.: Multi-focus image fusion using Stationary Wavelet Transform (SWT) with Principal Component Analysis (PCA). In: 10th International Conference on Electrical and Electronics Engineering (ELECO), 30 Nov–2 Dec 2017

  25. Muhammad, S.F., Arif, M., Somaya, A.A.M.: Multi-focus image fusion using content adaptive blurring. Inform. Fusion 45, 96–112 (2019)

    Article  Google Scholar 

  26. Aymaz, S., Köse, C.: A novel image decomposition-based hybrid technique with super-resolution method for multi-focus image fusion. Inform. Fusion 45, 113–127 (2019)

    Article  Google Scholar 

  27. Naidu, V.P.S., Raol, J.R.: Pixel-level image fusion using wavelets and principal component analysis. Def. Sci. J. 58(3), 338–352 (2008)

    Article  Google Scholar 

  28. Jagalingam, P., Hegde, A.V.: a review of quality metrics for fused image. In: International Conference on Water Resources, Coastal and Ocean Engineering (ICWRCOE 2015), Aquatic Procedia 4, pp 133–142

  29. Liu, Yu., Xun Chen, Hu., Peng, and Zengfu Wang. : Multi-focus image fusion with a deep convolutional neural network. Information Fusion 36, 191–207 (2017)

    Article  Google Scholar 

  30. Dynamic weighted discrimination power analysis: a novel approach for face and palmprint recognition in DCT domain. Int. J. Phys. Sci., 5(17), 2543–2554 (2010)

  31. Leng, L., Zhang, J.: Palmhash code vs. palmphasor code. Neurocomputing 108, 1–12 (2013)

    Article  Google Scholar 

  32. Leng, L., Li, M., Kim, C., Bi, X.: Dual-source discrimination power analysis for multi-instance contactless palmprint recognition. Multimed. Tools Appl. 76(1), 333–354 (2017)

    Article  Google Scholar 

  33. Martin, E.: Novel method for stride length estimation with body area network accelerometers. In: 2011 IEEE Topical Conference on Biomedical Wireless Technologies, Networks, and Sensing Systems. IEEE, pp 79–82 (2011)

  34. Broughton, S. A.: Wavelet Based Methods in Image Processing. www.rose-hulman.edu. Accessed 02 May 2017

  35. Akansu, A.N., Smith, M.J.T.: Subband and wavelet transforms: design and applications. Kluwer Academic Publishers (1995)

    Google Scholar 

  36. Akansu, A.N., Medley, M.J.: Wavelet, subband and block transforms in communications and multimedia. Kluwer Academic Publishers (1999)

    MATH  Google Scholar 

  37. Akansu, A.N., Duhamel, P., Lin, X., de Courville, M.: Orthogonal transmultiplexers in communication: a review. IEEE Trans. Signal Process. 46(4), 979–995 (1998). (Special Issue on Theory and Applications of Filter Banks and Wavelets)

    Article  Google Scholar 

  38. Stationary wavelet transform, Available at: https://en.wikipedia.org/wiki/Stationary_wavelet_transform, Accessed 12 Dec 2021

  39. Zhang, Y.: Feature extraction of brain MRI by stationary wavelet transform and its applications. J. Biol. Syst. 18(s1), 115–132 (2010). https://doi.org/10.1142/S0218339010003652

    Article  MATH  Google Scholar 

  40. Dong, Z.: Magnetic resonance brain image classification via stationary wavelet transform and generalized Eigenvalue proximal support vector machine. J. Med. Imaging Health Inform. 5(7), 1395–1403 (2015). https://doi.org/10.1166/jmihi.2015.1542

    Article  Google Scholar 

  41. Prabhishek, S., Raj, S.: A new homomorphic and method noise thresholding based despeckling of SAR image using anisotropic diffusion. J. King Saud Univ. Comp. Inform. Sci. (2017). https://doi.org/10.1016/j.jksuci.2017.06.006

    Article  Google Scholar 

  42. Singh, P., Shree, R.: A comparative study to noise models and image restoration techniques. Int. J. Comp. Appl. 149(1), 18 (2016)

    Google Scholar 

  43. Choongsang, C., Sangkeun, L.: Effective five directional partial derivatives-based image smoothing and a parallel structure design. IEEE Trans. Image Process. 25(4), 1617–1625 (2016). https://doi.org/10.1109/TIP.2016.2526785

    Article  MathSciNet  MATH  Google Scholar 

  44. Wang, Z., Simoncelli, E.P., Bovik, A.C.: Multiscale structural similarity for image quality assessment. In: Conference Record of the Thirty-Seventh Asilomar Conference on Signals, Systems and Computers, (2004). 2: 1398–1402. https://doi.org/10.1109/ACSSC.2003.1292216

  45. Shi, W., Zhu, C.Q., Tian, Y., Nichol, J.: Wavelet-based image fusion and quality assessment. Int. J. Appl. Earth Obs. Geoinform. 6(3–4), 241–251 (2005)

    Article  Google Scholar 

  46. Singh, P., Shree, R.: A new SAR image despeckling using directional smoothing filter and method noise thresholding. Eng. Sci. Tech. Int. J. (2018). https://doi.org/10.1016/j.jestch.2018.05.009

    Article  Google Scholar 

  47. Perona, P., Malik, J.: Scale space and edge detection using anisotropic diffusion. IEEE Trans. Image Process. 12(8), 629639 (1990)

    Google Scholar 

  48. Samadzadegan, F., Dadrasjavan, F.: Evaluating the sensitivity of image fusion quality metrics to image degradation in satellite imagery. J. Indian Soc. Remote Sens. (2011). https://doi.org/10.1007/s12524-011-0117-z

    Article  Google Scholar 

  49. Basic Intensity Quantification with ImageJ, Available at: https://www.unige.ch/medecine/bioimaging/files/1914/1208/6000/Quantification.pdf, Accessed 01 Jan 2021

  50. Calculate Standard Deviation, Available at: https://explorable.com/calculate-standard-deviation, Accessed 20 Jan 2021

  51. Multi focus image dataset, Available at: http://mansournejati.ece.iut.ac.ir/content/lytro-multi-focus-dataset, Accessed 20 Jan 2021

  52. Naidu, V.P.S.: Image fusion technique using multi-resolution singular value decomposition. Def. Sci. J. 61(5), 479–484 (2011)

    Article  MathSciNet  Google Scholar 

  53. Liu, Y., Liu, S., Wang, Z.: A general framework for image fusion based on multi-scale transform and sparse representation. Inf. Fusion 24, 147–164 (2015)

    Article  Google Scholar 

  54. Nejati, M., Samavi, S., Shirani, S.: Multi-focus image fusion using dictionary-based sparse representation. Inform. Fusion 25, 72–84 (2015)

    Article  Google Scholar 

  55. Kumar, B.K.S.: Image fusion based on pixel significance using cross bilateral filter. Signal Image Video Process. 9(5), 1193–1204 (2015)

    Article  Google Scholar 

  56. Fu, G.P., Hong, S.H., Li, F.L., Wang, L.: A novel multi-focus image fusion method based on distributed compressed sensing. J. Vis. Commun. Image Represent. 67, 102760 (2020)

    Article  Google Scholar 

  57. Liu, Z., Chai, Yi., Yin, H., Zhou, J., Zhu, Z.: A novel multi-focus image fusion approach based on image decomposition. Inform. Fusion 35, 102–116 (2017)

    Article  Google Scholar 

  58. Guo, R., Shen, X.J., Dong, X.Y., Zhang, X.L.: Multi-focus image fusion based on fully convolutional networks. Front. Inform. Technol. Electron. Eng. 21(7), 1019–1033 (2020)

    Article  Google Scholar 

  59. Farid, M.S., Mahmood, A., Al-Maadeed, S.A.: Multi-focus image fusion using content adaptive blurring. Inform. Fusion 45, 96–112 (2019)

    Article  Google Scholar 

  60. Goshtasby, A.A., Nikolov, S.: Guest editorial: image fusion: advances in the state of the art. Inform. Fusion 8(2), 114–118 (2007)

    Article  Google Scholar 

  61. Multi focus image dataset, Available at: http://dsp.etfbl.net/mif/, Accessed 20 Jan 2021

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Authors and Affiliations

  1. Department of CSE, Amity School of Engineering and Technology, Amity University, Noida, Uttar Pradesh, India

    Prabhishek Singh & Achyut Shankar

  2. Department of CSE, Graphic Era Deemed To Be University Dehradun, Dehradun, Uttarakhand, India

    Manoj Diwakar

  3. Department of Computer Science, Middlesex University, London, UK

    Xiaochun Cheng

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  1. Prabhishek Singh
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  4. Achyut Shankar
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Correspondence to Achyut Shankar.

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Singh, P., Diwakar, M., Cheng, X. et al. A new wavelet-based multi-focus image fusion technique using method noise and anisotropic diffusion for real-time surveillance application. J Real-Time Image Proc 18, 1051–1068 (2021). https://doi.org/10.1007/s11554-021-01125-8

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