Your browser does not support JavaScript!
http://iet.metastore.ingenta.com
1887

access icon free Enhanced image fusion using directive contrast with higher-order approximation

© The Institution of Engineering and Technology
Received 08/01/2017, Accepted 07/11/2017, Revised 29/09/2017, Published 14/11/2017

One of the most obvious characteristics of a good quality fused image is high spectral resolution. It is used to discriminate inner information in an image of low and high resolution by computing respective frequency components. The high-resolution image results in high correlation between the adjacent bands. The authors have utilised resolution, contrast, and correlation using the wavelet fusion approach to achieve the enhanced image. A wavelet scheme is used to extract low- and high-frequency data, forming wavelet domain components. These components are combined to create a fused wavelet coefficient map. To further improve the fused coefficients, we have used a higher-order approximation to compute directive details of an image. The high-frequency components are extracted to obtain edge and texture information to avoid too much spectral information loss because block-wise wavelet transform decomposition and reconstruction introduces visible blocking artefacts in the image. In this work, phase variation in wavelets is also discussed and error analysis is performed. Comparison of various performance measures using different methods is shown in both qualitative and quantitative results.

Inspec keywords: image fusion; approximation theory; image enhancement; image resolution; wavelet transforms

Other keywords: wavelet domain components; higher-order approximation; directive contrast; block-wise wavelet transform decomposition; high spectral resolution; enhanced image fusion

Subjects: Computer vision and image processing techniques; Integral transforms; Interpolation and function approximation (numerical analysis); Interpolation and function approximation (numerical analysis); Optical, image and video signal processing; Integral transforms

References

    1. 1)
      • 4. Zhuozheng, W., Deller, J.R.: ‘An improved multi-sensor image fusion algorithm’. Int. Conf. on Identification, Information and Knowledge in the Internet of Things (IIKI), Beijing, 17–18 October 2014, pp. 146–151.
    2. 2)
      • 21. Mallat, S.: ‘A wavelet tour of signal processing’ (Academic Press, Society for Industrial and Applied Mathematics (SIAM), 1999, 2nd edn.).
    3. 3)
      • 3. Daubechies, I.: ‘Orthonormal bases of compactly supported wavelets II, variations on a theme’, SIAM J. Math. Anal., 1993, 24, (2), pp. 499–519.
    4. 4)
      • 9. Tong, C.S., Leung, K.T.: ‘Super-resolution reconstruction based on linear interpolation of wavelet coefficients’, Multidimens. Syst. Signal Process., 2007, 18, (2), pp. 153–171.
    5. 5)
      • 18. Patil, V.D., Ruikar, S.D.: ‘Image enhancement by wavelet with principal component analysis’, Int. J. Adv. Innov. – Thoughts Ideas, 2012, 1, (3), pp. 1–9.
    6. 6)
      • 6. Yong, Y., Dongsun, P., Shuying, H., et al: ‘Medical image fusion via an effective wavelet based approach’, EURASIP J. Adv. Signal Process., 2010, 2010, pp. 1–13, Article no. 44.
    7. 7)
      • 32. Fuhr, H., Wild, M.: ‘Characterizing wavelet coefficient decay of discrete-time signals’, Appl. Comput. Harmon. Anal., 2006, 20, (2), pp. 184–201.
    8. 8)
      • 23. Singh, R., Srivastava, R., Prakash, O., et al: ‘Multi-modal medical image fusion using Daubechies complex wavelet transform’. Int. Conf. on Informatics, Electronics & Vision (ICIEV), Dhaka, 18–19 May 2012, pp. 304–309.
    9. 9)
      • 19. Bhavana, V., Krishnappa, H. K.: ‘Multi-modality medical image fusion using discrete wavelet transform’. 4th Int. Conf. on Eco-friendly Computing and Communication Systems (ICECCS), Procedia Computer Science, November 2015, vol. 70, pp. 625–631.
    10. 10)
      • 1. Petrou, M., Petrou, C.: ‘Image processing: the fundamentals’ (Wiley, Hoboken, NJ, 2010).
    11. 11)
      • 2. Mallat, S., Hwang, W.L.: ‘Singularity detection and processing with wavelets’, IEEE Trans. Inf. Theory, 1992, 38, (2), pp. 617–643.
    12. 12)
      • 27. Donoho, D.L.: ‘Denoising by soft-thresholding’, IEEE Trans. Inf. Theory, 1995, 41, (3), pp. 613–627.
    13. 13)
      • 38. Pajares, G., Cruz, J.M.: ‘A wavelet-based image fusion tutorial’, Elsevier J. Pattern Recognit., 2004, 37, (8), pp. 1855–1872.
    14. 14)
      • 10. Li, S., Yang, B., Hu, J.: ‘Performance comparison of different multiresolution transforms for image fusion’, Inf. Fusion, 2011, 12, (2), pp. 74–84.
    15. 15)
      • 17. Zheng, H., Zheng, D., Yanxianag, H.U., et al: ‘Study on the optimal parameters of image fusion based on wavelet transform’, J. Comput. Inf. Syst., 2010, 6, (1), pp. 131–137.
    16. 16)
      • 15. John, J.L., Robert, J.O.C., Stavri, G.N., et al: ‘Pixel and region based image fusion with complex wavelets’, Inf. Fusion, 2007, 8, (2), pp. 119–130.
    17. 17)
      • 13. Xiao, Z., Zheng, C.: ‘Medical image fusion based on an improved wavelet coefficient contrast’. 3rd IEEE Int. Conf. on Bioinformatics and Biomedical Engineering (ICBBE), Beijing, 11–13 June 2009, pp. 1–4.
    18. 18)
      • 39. Kannan, K., Perumal, S.A., Arulmozhi, K.: ‘Performance comparison of various levels of fusion of multi-focused images using wavelet transform’, Int. J. Comput. Appl., 2010, 1, (6), pp. 71–78.
    19. 19)
      • 20. Singh, R., Khare, A.: ‘Fusion of multimodal medical images using Daubechies complex wavelet transform – a multiresolution approach’, Spec. Issue Inf. Fusion Med. Image Comput. Syst., 2014, 19, pp. 49–60.
    20. 20)
      • 8. Cheng, S., Junmin, H.E., Zhongwei, L.V.: ‘Medical image of PET/CT weighted fusion based on wavelet transform’. 2nd Int. Conf. on Bioinformatics and Biomedical Engineering (ICBBE 2008), Shanghai, 16–18 May 2008, pp. 2523–2525.
    21. 21)
      • 40. Ahmadvand, A., Daliri, M.R.: ‘Invariant texture classification using a spatial filter bank in multi-resolution analysis’, Image Vis. Comput., 2016, 45, pp. 1–10.
    22. 22)
      • 16. Miao, Q., Shi, C., Xu, P., et al: ‘A novel algorithm of image fusion using shearlets’, Opt. Commun., 2011, 284, (6), pp. 1540–1547.
    23. 23)
      • 5. Hill, P.R., Canagarajah, C.N., Bull, D.R.: ‘Image fusion using complex wavelets’. Electronic Proc. 13th British Machine Vision Conf., Wales, 2–5 September 2002, pp. 487–496.
    24. 24)
      • 7. Saeedi, J., Faez, K.: ‘Infrared and visible image fusion using fuzzy logic and population based optimization’, Appl. Soft Comput., 2012, 12, (3), pp. 1041–1054.
    25. 25)
      • 22. Daubechies, I.: ‘Ten lectures on wavelets’ (Society for Industrial and Applied Mathematics, Philadelphia, USA, 1992).
    26. 26)
      • 24. Luo, X., Zhang, J., Dai, Q.: ‘A regional image fusion based on similarity characteristics’, Signal Process., 2012, 92, (5), pp. 1268–1280.
    27. 27)
      • 12. Bruno, A., Mario, C., DePietro, G.: ‘A wavelet based algorithm for multimodal medical image fusion’. Springer Semantic Multimedia, 2007 (LNCS, 4816), pp. 117–120.
    28. 28)
      • 26. Liu, H., Deng, M., Xiao, C., et al: ‘Image fusion algorithm based on adaptive weighted coefficients’. 10th IEEE Int. Conf. on Signal Processing (ICSP), Beijing, 24–28 October 2010, pp. 748–751.
    29. 29)
      • 35. Balan, R., Wang, Y.: ‘Invertibility and robustness of phaseless reconstruction’, Appl. Comput. Harmon. Anal., 2015, 38, (3), pp. 469–488.
    30. 30)
      • 11. Bute, P., Sagar, R.: ‘Performance evaluation of contrast based image fusion technique over medical image’. 2012 Annual IEEE India Conf. (INDICON), Kochi, December 2012, pp. 1184–1188.
    31. 31)
      • 31. Kim, S.S., Eom, K., Kim, Y.S.: ‘Wavelet image interpolation using approximate modelling of exponential decay’. 8th IEEE Workshop on Multimedia Signal Processing, Canada, 3–6 October 2006, pp. 360–363.
    32. 32)
      • 25. Xuan, S.B., Sang, G.L., Zhao, B., et al: ‘Non-sampling contourlet based consistency verification method of image fusion’. Advanced Intelligent Computing, 2012 (LNCS, 6838), pp. 342–349.
    33. 33)
      • 30. Mihcak, M.K., Kozintsev, I., Ramchandran, K., et al: ‘Low-complexity image denoising based on statistical modelling of wavelet coefficients’, IEEE Signal Process. Lett., 1999, 6, (12), pp. 300–303.
    34. 34)
      • 14. Shen, R., Cheng, I., Basu, A.: ‘Cross-scale coefficient selection for volumetric medical image fusion’, IEEE Trans. Biomed. Eng., 2012, 60, (4), pp. 1069–1079.
    35. 35)
      • 28. Johnston, I.M., Silverman, B.W.: ‘Wavelet threshold estimators for data with correlated noise’, J. R. Stat. Soc. B, 1997, 59, (2), pp. 319–351.
    36. 36)
      • 34. Anden, J., Mallat, S.: ‘Multiscale scattering for audio classification’. Proc. of the Int. Society for Music Information Retrieval Conf., Miami, Florida, USA, 24–28 October 2011, pp. 657–662.
    37. 37)
      • 36. Balan, R., Casazza, P., Edidin, D.: ‘On signal reconstruction without phase’, Appl. Comput. Harmon. Anal., 2006, 20, (3), pp. 345–356.
    38. 38)
      • 37. Bruna, J.: ‘Scattering representations for recognition’. PhD thesis, Ecole Polytechnique, Palaiseau, 2013.
    39. 39)
      • 33. Mesgarani, N., Slaney, M., Shamma, S.A.: ‘Discrimination of speech from nonspeech based on multiscale spectrotemporal modulations’, IEEE Trans. Audio, Speech, Lang. Process., 2006, 14, (3), pp. 920–930.
    40. 40)
      • 29. Chen, M.S.: ‘A new method of image denoising based on wavelet transform’, Opt. Tech., 2006, 32, (5), pp. 796–798.
http://iet.metastore.ingenta.com/content/journals/10.1049/iet-spr.2016.0757
Loading

Related content

content/journals/10.1049/iet-spr.2016.0757
pub_keyword,iet_inspecKeyword,pub_concept
6
6
Loading
Print this page
This is a required field
Please enter a valid email address