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Multimedia Tools and Applications

, Volume 78, Issue 2, pp 1235–1263 | Cite as

SWT and PCA image fusion methods for multi-modal imagery

  • Rabia Bashir
  • Riaz Junejo
  • Nadia N. QadriEmail author
  • Martin Fleury
  • Muhammad Yasir Qadri
Article
First Online:
  • 98 Downloads

Abstract

Image fusion is the process of combining two or more related images to produce a single output image, containing more relevant information than any one of the input images. The image-fusion process depends upon: the application domain; the number of images undergoing fusion; and the type of imagery, such as whether it is multi-spectral or multi-modal. For clarity of presentation, this paper takes two important fusion methods, Stationary Wavelet Transform (SWT) and Principal Components Analysis (PCA), and applies them to a variety of imagery. Results show that in multi-modal image fusion, PCA appears to perform better for those input images that have different contrast/brightness levels. SWT appears to give better performance when the input images are multi-modal and multi-sensor. A feature of the paper are the number of objective functions employed to evaluate the SWT and PCA methods, allowing the utility of each to be judged. The reader will also find in this paper a concise guide to image fusion techniques with clear recommendations on how to evaluate them.

Keywords

Image fusion Multi-modal PCA SWT 
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References

  1. 1.
    Abdi H, Williams LJ (2010) Principal component analysis. Wiley Interdiscip Rev Comput Statist 2:433–459Google Scholar
  2. 2.
    Al-Azzawi N, Abdullah WAKW (2011) Medical image fusion schemes using Contourlet transform and pca bases. In: Image fusion and its applications, pp 93–110Google Scholar
  3. 3.
    Al-Wassai F, Kalyankar N, Al-Zaky A (2011) Arithmetic and frequency filtering methods of pixel-based image fusion techniques. Int J Comput Sci 8(3):113–122Google Scholar
  4. 4.
    Al-Wassai F, Kalyankar N, Al-Zaky A (2011) Multisensor images fusion based on feature-level. Int J of Latest Tehnol 1(5):124–138Google Scholar
  5. 5.
    Alfano B, Ciampi M, De Pietro G (2007) A wavelet-based algorithm for multimodal medical image fusion. In: 2nd Int. Conf. on semantic and digital multimedia technol., pp 117–120Google Scholar
  6. 6.
    Babu B, Ch V, Kumar N, Vivekan K, Swamy A (2012) Comparison and improvement of wavelet based image fusion. Int J Comput Eng Manag 15(3):15–19Google Scholar
  7. 7.
    Bedi S, Agarwal J, Agarwal P (2013) Image fusion techniques and quality assessment parameters for clinical diagnosis: a review. Int J Adv Res Comput Commun Eng 2(2):1153–1157Google Scholar
  8. 8.
    Bharath B, Kanmani M (2017) Swarm intelligence based image fusion for thermal and visible images. In: Int. Conf. on Comput. of Power, Energy, Info. and Commun., pp 43–48Google Scholar
  9. 9.
    Bindu C, Prasad D (2012) Performance analysis of multi source fused medical images using multiresolution transforms. Int J Adv Comput Sci 3:54–62Google Scholar
  10. 10.
    Carper W, Lillesand T, Kiefer R (1990) The use of Intensity-Hue-Saturation transform for merging SPOT panchromatic and multispectral image data. Photogramm Eng Remote Sens 56(4):459–467Google Scholar
  11. 11.
    Daneshvar S, Ghassemian H (2010) MRI and PET image fusion by combining IHS and retina-inspired models. Info Fusion 11(2):114–123Google Scholar
  12. 12.
    Das S, Kundu MK (2013) A neuro-fuzzy approach for medical image fusion. IEEE Trans Biomed Eng 60(12):3347–3353Google Scholar
  13. 13.
    Das S, Chowdhury M, Kundu M (2011) Medical image fusion based on Ripplet transform type-I. Prog Electromagn Res 30:355–370Google Scholar
  14. 14.
    Deshmukh M, Udhav B (2010) Image fusion and image quality assessment of fused images. Int J Image Process 4(5):484–508Google Scholar
  15. 15.
    Divya R, Palraj K (2014) Survey on multimodal image fusion using stationary wavelet transform and fuzzy logic. Int J Sci Technol Eng, 1(5)Google Scholar
  16. 16.
    Divyaloshini V, Saraswathi M (2014) Performance evaluation of image fusion techniques and its implementation in biometric recognition. Int J Technol Enhanc Emerg Eng 2(3):25–32Google Scholar
  17. 17.
    Ehlers M, Klonus S (2008) Quality assessment for multitemporal and multisensor image fusion. In: Proceedings of SPIE, vol 71100: Remote Sensing, pp 1–9Google Scholar
  18. 18.
    El Ejaily A, Eltohamy F, El Nahas M, Ismail G (2013) A new image fusion technique to improve the quality of remote sensing images. Int J Comput Sci Issues 10(3(1))Google Scholar
  19. 19.
    Gawari N, Lalitha Y (2014) Comparative analysis of PCA , DCT & DWT based image fusion techniques. Int J Emerg Res Manag Technol 3(5):54–61Google Scholar
  20. 20.
    Godse DA, Bormane DS (2011) Wavelet based image fusion using pixel based maximum selection rule. Int J of Eng Sci and Technol 3(7):5572–5577Google Scholar
  21. 21.
    González-Audícana M, Saleta J, Catalán R, García R (2004) Fusion of multispectral and panchromatic images using improved IHS and PCA mergers based on wavelet decomposition. IEEE Trans Geosci Remote Sens 42(6):1291–1299Google Scholar
  22. 22.
    Gupta C, Gupta P (2015) A study and evaluation of transform domain based image fusion techniques for visual sensor networks. Int J of Comput Apps 116(8):26–30Google Scholar
  23. 23.
    Gupta A, Cheeran A, Nikose M (2011) Image restoration using wavelet based image fusion. Int J of Eng Sci and Technol 3(2):1388–1394Google Scholar
  24. 24.
    Haghighat MA, Aghagolzadeh A, Seyedarabi H (2011) Multi-focus image fusion for visual sensor networks in DCT domain. Comput Electric Eng 37(5):789–797zbMATHGoogle Scholar
  25. 25.
    He C, Liu Q, Li H, Wang H (2010) Multimodal medical image fusion based on IHS and PCA. Procedia Eng 7:280–285Google Scholar
  26. 26.
    Indhumadhi N, Padmavathi G (2011) Enhanced image fusion algorithm using Laplacian pyramid and spatial frequency based wavelet algorithm. Int J Soft Comput Eng 1(5):298–303Google Scholar
  27. 27.
    Jolliffe I (2008) Principal component analysis, 2nd edn. Springer, BerlinzbMATHGoogle Scholar
  28. 28.
    Kim YM, Theobalt C, Diebel J, Kosecka J, Miscusik B, Thrun S (2009) Multi-view image and tof sensor fusion for dense 3D reconstruction. In: IEEE Int. Conf. on computer vision workshops, pp 1542–1549Google Scholar
  29. 29.
    Li S, Kang X, Fang L, Hu J, Yin H (2017) Pixel-level image fusion: a survey of the state of the art. Inform Fus 33:100–112Google Scholar
  30. 30.
    Lin B, Tao X, Duan Y, Lu J (2015) Perceptual-based hyperspectral image fusion using multiresolution analysis. IEEE Access, 14(8)Google Scholar
  31. 31.
    Maes F, Vandermeulen D, Suetens P (2003) Medical image registration using mutual information. Proc IEEE 91(10):1699–1722zbMATHGoogle Scholar
  32. 32.
    Mahajan S, Singh A (2014) A comparative analysis of different image fusion techniques. Int J Comput Sci 2(1):8–15Google Scholar
  33. 33.
    Mahajan S, Singh A (2014) Integrated PCA & DCT based fusion using consistency verification & non-linear enhancement. Int J Eng Comput Sci 3(3):4030–4039Google Scholar
  34. 34.
    Mandhare RA, Upadhyay P, Gupta S (2013) Pixel-level image fusion using Brovey and wavelet transform. Int J Adv Res Electr Electron Instrum 2(6):2690–2695Google Scholar
  35. 35.
    Mifdal J, Coll B, Courty N, Froment J, Vedel B (2017) Hyperspectral and multispectral Wasserstein barycenter for image fusion. In: IEEE Geoscience and remote sensing symp., pp 3373–3376Google Scholar
  36. 36.
    Mirajkar PP, Ruikar S (2013) Image fusion based on stationary wavelet transform. Int J Adv Eng Res Stud 2(4):99–101Google Scholar
  37. 37.
    Morris C, Rajesh R (2014) Survey of spatial domain image fusion techniques. Int J Adv Research in Comp Sci Info Technol 3(3):249–254Google Scholar
  38. 38.
    Naidu V, Raol J (2008) Pixel-level image fusion using wavelets and principal component analysis. Def Sci J 58(3):338–352Google Scholar
  39. 39.
    Nair S, Aruna P, Vadivukarassi M (2013) PCA based image fusion of face and iris biometric features. Int J Adv Comput Theory Eng 1(2):106–112Google Scholar
  40. 40.
    Nunez J, Otazu X, Fors O, Prades A, Pala V, Arbiol R (1999) Multiresolution-based image fusion with additive wavelet decomposition. IEEE Trans Geosci Remote Sens 37(3):1204–1211Google Scholar
  41. 41.
    Pardnya M, Ruikar S (2012) Image fusion method based on WPCA. Int J Adv Res Comput Sci Softw Eng 2(5):1–4Google Scholar
  42. 42.
    Parvatikar MV, Phadke G (2014) Comparative study of different image fusion techniques. Int J Sci Eng Technol 3(4):375–379Google Scholar
  43. 43.
    Sadhasivam S, Keerthivasan M, Muttan S (2011) Implementation of max principle with PCA in image fusion for surveillance and navigation application. Electron Lett Comput Vis Image Anal 10(1):1–10Google Scholar
  44. 44.
    Sahu D, Parsai M (2012) Different image fusion techniques - a critical review. Int J Mod Eng Res 2(5):4298–4301Google Scholar
  45. 45.
    Sahu A, Bhateja V, Krishn A et al. (2014) Medical image fusion with Laplacian pyramids. Int Conf on Medical Imaging, m-Health and Emerging Commun Syst, 448–453Google Scholar
  46. 46.
    Sale D, Joshi M, Sapkal A (2012) DCT, and DWT based image fusion for robust face recognition. Int J Eng Res Appl 2(1):686–692Google Scholar
  47. 47.
    Savitha V, Kadhambari T, Sheeba R (2014) Multimodality medical image fusion using NSCT. Int J Res Eng Adv Technol 1(6):1–4Google Scholar
  48. 48.
    Shabanzade F, Ghassemian H (2017) Combination of wavelet and contourlet transforms for PET and MRI image fusion. In: Artificial Intelligence and signal processing conference, pp 178–183Google Scholar
  49. 49.
    Siddiqui AB, Jaffar MA, Hussain A, Mirza AM (2011) Block-based pixel level multi-focus image fusion using particle swarm optimization. Int J Innov Comput Inf Control 7(7):3583–3596Google Scholar
  50. 50.
    Svab A, Ostir K (2006) High-resolution image fusion. Photogram Eng Remote Sens 72(5):565–572Google Scholar
  51. 51.
    Tang M, Nie F, Jain R (2017) A graph regularized dimension reduction method for out-of-sample data. Neurocomputing 255:58–63Google Scholar
  52. 52.
    Tank V, Shah D, Vyas T, Chotaliya S, Manavadaria M (2013) Image fusion based on Wavelet and Curvelet transform. IOSR J VLSI Signal Process 1(5):32–36Google Scholar
  53. 53.
    Tian J, Chen L (2012) Adaptive multi-focus image fusion using a wavelet-based statistical sharpness measure. Signal Process 92(9):2137–2146Google Scholar
  54. 54.
    Vekkot S, Shukla P (2009) A novel architecture for wavelet based image fusion. World Acad Sci Eng Technol 57:372–377Google Scholar
  55. 55.
    Wakure S, Todmal S (2013) Survey on different image fusion techniques. IOSR J VLSI Signal Process 1(6):42–48Google Scholar
  56. 56.
    Wan T, Canagarajah N, Achim A (2008) Compressive image fusion. In: IEEE Int. Conf. Image Process., pp 1308–1311Google Scholar
  57. 57.
    Wang Y (2013) Image fusion based on nonsubsampled contourlet transform and principal component analysis. J Converg Inf Technol 8(8):179–186Google Scholar
  58. 58.
    Wang Z, Ma Y (2008) Medical image fusion using m-PCNN. Info Fusion 9 (2):176–185Google Scholar
  59. 59.
    Wang J, Zhou D, Costas A, Li D, Li Q (2005) A comparative analysis of image fusion methods. IEEE Trans Geosci Remote Sens 43(6):1391–1402Google Scholar
  60. 60.
    Wang N, Ma Y, Zhan K, Yuan M (2013) Multimodal medical image fusion framework based on simplified PCNN in nonsubsampled contourlet transform domain. J Multimed 8(3):270–276Google Scholar
  61. 61.
    Wang Y, Lin X, Wu L, Zhang W, Zhang Q, Huang X (2015) Robust subspace clustering for multi-view data by exploiting correlation consensus. IEEE Trans Image Process 24(11):3939–3949MathSciNetGoogle Scholar
  62. 62.
    Wang Y, Wu L, Lin X, Zhao X (2017) Unsupervised metric fusion over multi-view data by graph random walk based cross-view diffusion. IEEE Trans Neural Netw Learn Syst 28(1):57–70Google Scholar
  63. 63.
    Wang Y, Wu L, Lin X, Gao J (2018) Multi-view spectral clustering via structured low-rank matrix factorization. IEEE Trans Neural Networks and Learning SystGoogle Scholar
  64. 64.
    Wilson T, Rogers S, Myers L (1995) Perceptual-based hyperspectral image fusion using multiresolution analysis. Opt Eng, 34(11)Google Scholar
  65. 65.
    Yang W, Wang J, Guo J (2013) A novel algorithm for satellite images fusion based on compressed sensing and PCA. Math Probl Eng, 10Google Scholar
  66. 66.
    Yin H, Li S (2011) Multimodal image fusion with joint sparsity model. Opt Eng 50(6):1–11Google Scholar
  67. 67.
    Zhang Q, Liu Y, Blum RS, Han J, Tao D (2017) Sparse representation based multi-sensor image fusion: a review. Inform Fus 40:57–75Google Scholar
  68. 68.
    Zhanga Z, Ma A, Hui Liu H, Gong Y (2009) Sparse representation based multi-sensor image fusion: a review. Comput Math Appl 57:1265–1271MathSciNetGoogle Scholar
  69. 69.
    Zheng Y, Essock EA, Hansen B (2004) An advanced image fusion algorithm based on wavelet transform — incorporation with PCA and morphological processing. In: Image Process: algorithms and systems III, pp 177–187Google Scholar
  70. 70.
    Zhou X, Yin X, Liu RA, Wang W (2013) Infrared and visible image fusion technology based on directionlets transform. EURASIP J Wireless Commun Network 2013(1):1–4Google Scholar

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© Springer Science+Business Media, LLC, part of Springer Nature 2018

Authors and Affiliations

  1. 1.COMSATS Institute of Information TechnologyWah Cantt.Pakistan
  2. 2.School of Computer Science and Electronic EngineeringUniversity of EssexColchesterUK

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