Multifocus image fusion scheme based on discrete cosine transform and spatial frequency

  • Vakaimalar E
  • Mala K
  • Suresh Babu R


Multifocus images are different images of the same scene captured with different focus in the cameras. These images when considered individually may not give good quality. Hence to obtain a good quality image, this work proposes an algorithm for fusing multifocus images using Discrete Cosine Transform and spatial frequency. The proposed algorithm works for fusing any number of images. The second step calculates the average and maximum of all the source images and reduces the source images to be processed as two. Then Discrete Cosine Transform (DCT) is applied over the two input images. Min-Max normalization is done on the DCT coefficients and fusion is done using spatial frequency. Inclusion of the second step of the proposed algorithm in some existing algorithms such as Stationary Wavelet Transform, Principal Component Analysis and spatial fusion improves the performance. The metrics used for evaluation proves that the proposed algorithm gives better results than the other algorithms using DCT and state of the art techniques.


Image fusion DCT Spatial frequency Min-Max normalization 



  1. 1.
    Aishwarya N, Thangammal B (2017) An image fusion framework using novel dictionary based sparse representation. Multimed Tools Appl 76(21):21869–21888. CrossRefGoogle Scholar
  2. 2.
    Aslantas V, Kurban R (2010) Fusion of multi-focus images using differential evolution algorithm. Expert Syst Appl 37:8861–8870CrossRefGoogle Scholar
  3. 3.
    Bagher M, Haghighat A, Aghagolzadeh A, Seyedarabi H (2011) Multi-focus image fusion for visual sensor networks in DCT domain. Comput Electr Eng 37:789–797CrossRefGoogle Scholar
  4. 4.
    Cao L, Jin L, Tao H, Liu G, Zhuang Z, Zhang Y (2015) Multi-focus image fusion based on spatial frequency in discrete cosine transform domain. IEEE Signal Process Lett 22(2):220–223CrossRefGoogle Scholar
  5. 5.
    Darji AD, Kushwah SS, Merchant SN, Chandorkar AN (2014) High-performance hardware architectures for multi-level lifting-based discrete wavelet transform. EURASIP J Image Video Process.
  6. 6.
    Ellmauthaler A, Pagliari CL, da Silva EAB (2013) Multiscale image fusion using the undecimated wavelet transform with spectral factorization and nonorthogonal filter banks. IEEE Trans Image Process 22(3):1005–1017MathSciNetCrossRefGoogle Scholar
  7. 7.
    Esteban J, Starr A, Willetts R, Hannah P, Bryanston-Cross P (2005) A review of data fusion models and architectures: towards engineering guidelines. Neural Comput & Applic.
  8. 8.
    Han J, Kamber M (2006) Data mining concepts and techniques. Elsevier, Noida, pp 70–71Google Scholar
  9. 9.
    Hang R, Liu Q, Song H, Sun Y (2016) Matrix-based discriminant subspace ensemble for hyperspectral image spatial–spectral feature fusion. IEEE Trans Geosci Remote Sens 54(2):783–794CrossRefGoogle Scholar
  10. 10.
  11. 11.
    Jagalingam P, Hegde AV (2015) A review of quality metrics for fused image. Aquat Procedia 4:133–142CrossRefGoogle Scholar
  12. 12.
    Li S, Yang B (2008) Multifocus image fusion using region segmentation and spatial frequency. Image Vis Comput 26:971–979CrossRefGoogle Scholar
  13. 13.
    Li S, Kang X, Hu J (2013) Image fusion with guided filtering. IEEE Trans Image Process 22(7):2864–2875CrossRefGoogle Scholar
  14. 14.
    Li S, Kang X, Hu J, Yang B (2013) Image matting for fusion of multi-focus images in dynamic scenes. Inf Fusion 14:147–162CrossRefGoogle Scholar
  15. 15.
    Liu Y, Liu S, Wang Z (2015) Multi-focus image fusion with dense SIFT. Inf Fusion 23:139–155CrossRefGoogle Scholar
  16. 16.
    Liu Y, Chen X, Peng H, Wang Z (2017) Multi-focus image fusion with a deep convolutional neural network. Inf Fusion 36:191–207CrossRefGoogle Scholar
  17. 17.
    Liua C, Longa Y, Mao J (2016) Energy-efficient multi-focus image fusion based on neighbor distance and morphology. Optik 127(23):11354–11363CrossRefGoogle Scholar
  18. 18.
    Lu Q, Huang X, Zhang L (2016) A novel MRF-based multifeature fusion for classification of remote sensing images. IEEE Geosci Remote Sens Lett 13(4):515–519CrossRefGoogle Scholar
  19. 19.
    Naidu VPS (2011) Image fusion technique using multi-resolution singular value decomposition. Def Sci J 61(5):479–484MathSciNetCrossRefGoogle Scholar
  20. 20.
    Petrovic VS, Xydeas CS (2004) Gradient-based multiresolution image fusion. IEEE Trans Image Process 13(2):228–237CrossRefGoogle Scholar
  21. 21.
    Raol JR, Multi-sensor Data Fusion with Matlab (2010) CRC Press Taylor & Francis GroupGoogle Scholar
  22. 22.
    Siddiqui AB, Hussain A, Mirza AM (2010) Block-based feature-level multi-focus image fusion. Proc. Int. Conf. IEEE, pp. 6949–6957Google Scholar
  23. 23.
    Teng J, Wang X, Zhang J, Wang S, Huo P (2010) A multimodality medical image fusion algorithm based on wavelet transform. Adv Swarm Intell ICSI, Lect Notes Comput Sci 6146:627–633CrossRefGoogle Scholar
  24. 24.
    Vijayarajan R, Muttan S (2015) Discrete wavelet transform based principal component averaging fusion for medical images. Int J Electron Commun 69(6):896–902CrossRefGoogle Scholar
  25. 25.
    Wu H, Xing Y (2010) Pixel-based image fusion using wavelet transform for spot and etm image. Proc. Int. Conf. IEEE, pp. 936–940Google Scholar
  26. 26.
    Yan C, Zhang Y, Xu J, Dai F, Zhang J, Dai Q, Wu F (2014) Efficient parallel framework for HEVC motion estimation on many-core processors. IEEE Trans Circuits Syst Video Technol 24(12):2077–2089CrossRefGoogle Scholar
  27. 27.
    Yan C, Zhang Y, Xu J, Dai F, Li L, Dai Q, Wu F (2014) A highly parallel framework for HEVC coding unit partitioning tree decision on many-core processors. IEEE Signal Process Lett 21(5):573–576CrossRefGoogle Scholar
  28. 28.
    Yan C, Xie_ H, Chen J, Zha Z, Hao X, Zhang Y, Dai Q (2017) A fast Uyghur text detector for complex background images. IEEE Trans Multimedia 14(8).
  29. 29.
    Yan C, Xie H, Yang D, Yin J, Zhang Y, Dai Q (2018) Supervised hash coding with deep neural network for environment perception of intelligent vehicles. IEEE Trans Intell Transp Syst 19(1):284–295CrossRefGoogle Scholar
  30. 30.
    Yan C, Xie H, Liu S, Yin J, Zhang Y, Dai Q (2018) Effective Uyghur language text detection in complex background images for traffic prompt identification. IEEE Trans Intell Transp Syst 19(1):220–229CrossRefGoogle Scholar
  31. 31.
    Yang Y, Park DS, Huang S, Rao N (2010) Medical image fusion via an effective wavelet-based approach. EURASIP J Adv Signal Process.
  32. 32.
    Yang Y, Tong S, Huang S, Lin P (2015) Multifocus image fusion based on NSCT and focused area detection. IEEE Sensors J 15(5):2824–2838. CrossRefGoogle Scholar
  33. 33.
    Yu N, Qiu T, Bi F, Wang A (2011) Image features extraction and fusion based on joint sparse representation. IEEE J Sel Topics Signal Process 5(5):1074–1082CrossRefGoogle Scholar
  34. 34.
    Zhang Y, Ge L (2009) Efficient fusion scheme for multi-focus images by using blurring measure. Digital Signal Process 19:186–193CrossRefGoogle Scholar
  35. 35.
    Zhang Y, Shuihuawang YH, Wu L (2010) Feature extraction of brain MRI by stationary wavelet transform and its applications. J Biol Syst 18:115–132CrossRefGoogle Scholar
  36. 36.
    Zhanga Y, Chena L, Zhaoa Z, Jia J (2016) Multi-focus image fusion based on cartoon-texture image decomposition. Optik 127(3):1291–1296CrossRefGoogle Scholar
  37. 37.
    Zhao H, Shang Z, Tang YY, Fang B (2013) Multi-focus image fusion based on the neighbor distance. Pattern Recogn 46:1002–1011CrossRefGoogle Scholar
  38. 38.
    Zheng S, Shi WZ, Liu J, Zhu G-X, Tian JW (2007) Multi source image fusion using support value transform. IEEE Trans Image Process 16(7):1831–1839MathSciNetCrossRefGoogle Scholar
  39. 39.
    Zhou Z, Li S, Wang B (2014) Multi-scale weighted gradient-based fusion for multi-focus images. Inf Fusion 20:60–72CrossRefGoogle Scholar

Copyright information

© Springer Science+Business Media, LLC, part of Springer Nature 2019

Authors and Affiliations

  1. 1.Information Technology, Kamaraj College of Engineering and TechnologyVirudhunagarIndia
  2. 2.Computer Science and Engineering, Mepco Schlenk Engineering CollegeSivakasiIndia
  3. 3.Electronics and Communication Engineering, Kamaraj College of Engineering and TechnologyVirudhunagarIndia

Personalised recommendations