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Compressive sensing based simultaneous fusion and compression of multi-focus images using learned dictionary

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Abstract

In this paper, we present a framework of fusion and compression of multi-focus images using learned dictionary. A single dictionary, learned from a set of natural images is used to initially fuse the multi-focus images. Using the same dictionary as the basis matrix, the fused coefficients are compressed using compressive sensing theory. Recovery of the fused image using the compressively sensed measurements is carried out at the receiver end using well known Sl0 recovery algorithm. Fusion and compression is thus achieved simultaneously using a single learned dictionary. Experiments on multi-focus images show the effectiveness of the proposed method in fusing and compressing the images concurrently. Simulation results also verify that the proposed method outperforms some of the existing compression methods especially at lower sampling rates.

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References

  1. Aharon M, Elad M, Bruckstein A (2006) An algorithm for designing overcomplete dictionaries for sparse representation. IEEE Trans Signal Process 54(11):4311–4322

    Article  MATH  Google Scholar 

  2. Candes EJ (2008) The restricted isometry property and its implications for compressed sensing. CR Math 346(9-10):589–592

    MathSciNet  MATH  Google Scholar 

  3. Candes EJ, Tao T (2006) Near-optimal signal recovery from random projections: universal encoding strategies? IEEE Trans Inf Theory 52(12):5406–5425

    Article  MathSciNet  MATH  Google Scholar 

  4. Candes EJ, Wakin MB (2008) An introduction to compressive sampling. IEEE Signal Process Mag 25(2):21–30

    Article  Google Scholar 

  5. Chen G, Zhang J, Li D (2016) Fractional-order total variation combined with sparsifying transforms for compressive sensing sparse image reconstruction. J Vis Commun Image Represent 38:407–422

    Article  Google Scholar 

  6. Donoho DL (2006) Compressed sensing. IEEE Trans Inf Theory 52(4):1289–1306

    Article  MathSciNet  MATH  Google Scholar 

  7. George M, Thomas M, Jayadas CK (2016) A methodology for spatial domain image compression based on hops encoding. Procedia Technol 25:52–59

    Article  Google Scholar 

  8. http://decsai.ugr.es/cvg/CG/base.htm. Accessed June 2016

  9. http://home.ustc.edu.cn/~liuyu1/. Accessed March 2015

  10. http://sipi.usc.edu/database/database.php?volume=misc&image=22#top. Accessed Jan 2017

  11. Hassan SA, Hussain M (2011) Spatial domain lossless image data compression method. In: Information and Communication Technologies (ICICT), 2011 international conference on. IEEE 1-4

  12. Hu G, Xiao D, Wang Y, Xiang T (2017) An image coding scheme using parallel compressive sensing for simultaneous compression-encryption applications. J Vis Commun Image Represent 44:116–127

    Article  Google Scholar 

  13. Huang W, Jing Z (2007) Evaluation of focus measures in multi-focus image fusion. Pattern Recogn Lett 28(4):493–500

    Article  Google Scholar 

  14. Jagalingam P, Hegde AV (2015) A review of quality metrics for fused image. Aquat Procedia 4:133–142

    Article  Google Scholar 

  15. Ji X, Zhang G (2017) Image fusion method of SAR and infrared image based on Curvelet transform with adaptive weighting. Multimed Tools Appl 76(17):17633–17649

  16. Liu E, Temlyakov VN (2012) The orthogonal super greedy algorithm and applications in compressed sensing. IEEE Trans Inf Theory 58(4):2040–2047

    Article  MathSciNet  MATH  Google Scholar 

  17. Liu Y, Liu S, Wang Z (2015) Multi-focus image fusion with dense SIFT. Inf Fusion 23:139–155

    Article  Google Scholar 

  18. Liu Z, Yin H, Fang B, Chai Y (2015) A novel fusion scheme for visible and infrared images based on compressive sensing. Opt Commun 335:168–177

    Article  Google Scholar 

  19. Liu X, Mei W, Du H (2016) Simultaneous image compression, fusion and encryption algorithm based on compressive sensing and chaos. Opt Commun 366:22–32

    Article  Google Scholar 

  20. Mohimani H, Babaie-Zadeh M, Jutten C (2009) A fast approach for overcomplete sparse decomposition based on smoothed l0 norm. IEEE Trans Signal Process 57(1):289–301

    Article  MathSciNet  MATH  Google Scholar 

  21. Patel VM, Chellappa R (2011) Sparse representations, compressive sensing and dictionaries for pattern recognition. In: Pattern recognition (ACPR), 2011 first Asian conference on. IEEE 325-329

  22. Phamila AV, Amutha R (2013) Low complexity multifocus image fusion in discrete cosine transform domain. Opt Appl 43(4):693–706

    Google Scholar 

  23. Phamila AV, Amutha R (2013) Low complexity energy efficient very low bit-rate image compression scheme for wireless sensor network. Inf Process Lett 113(18):672–676

    Article  MathSciNet  MATH  Google Scholar 

  24. Phamila AV, Amutha R (2013) Low complex energy aware image communication in visual sensor networks. J Electron Imaging 22(4):041107–041107

    Article  Google Scholar 

  25. Phamila YAV, Amutha R (2014) Discrete cosine transform based fusion of multi-focus images for visual sensor networks. Signal Process 95:161–170

    Article  Google Scholar 

  26. Phamila AV, Amutha R (2015) Energy-efficient low bit rate image compression in wavelet domain for wireless image sensor networks. Electron Lett 51(11):824–826

    Article  Google Scholar 

  27. Shen Y, Li J, Zhu Z, Cao W, Song Y (2015) Image reconstruction algorithm from compressed sensing measurements by dictionary learning. Neurocomputing 151:1153–1162

    Article  Google Scholar 

  28. Xiao D, Wang L, Xiang T, Wang Y (2017) Multi-focus image fusion and robust encryption algorithm based on compressive sensing. Opt Laser Technol 91:212–225

    Article  Google Scholar 

  29. Yang B, Li S (2010) Multifocus image fusion and restoration with sparse representation. IEEE Trans Instrum Meas 59(4):884–892

    Article  Google Scholar 

  30. Yao S, Wang T, Shen W, Shaoming P, Chong Y (2017) Research of incoherence rotated chaotic measurement matrix in compressed sensing. Multimed Tools Appl 76(17):17699–17717

  31. Yuan H, Song H, Sun X, Guo K, Ju Z (2015) Compressive sensing measurement matrix construction based on improved size compatible array LDPC code. IET Image Process 9(11):993–1001

    Article  Google Scholar 

  32. Zhang Y, Chen L, Zhao Z, Jia J, Liu J (2014) Multi-focus image fusion based on robust principal component analysis and pulse-coupled neural network. Optik-Int J Light Electron Opt 125(17):5002–5006

    Article  Google Scholar 

  33. Zhang X, Lin H, Kang X, Li S (2014) Multi-modal image fusion with KNN matting. In: Chinese conference on pattern recognition. Springer, Berlin Heidelberg, p 89-96

  34. Zhang J, Zhao C, Zhao D, Gao W (2014) Image compressive sensing recovery using adaptively learned sparsifying basis via L0 minimization. Signal Process 103:114–126

    Article  Google Scholar 

  35. Zhao C, Guo Y, Wang Y (2015) A fast fusion scheme for infrared and visible light images in NSCT domain. Infrared Phys Technol 72:266–275

    Article  Google Scholar 

  36. Zhou N, Zhang A, Wu J, Pei D, Yang Y (2014) Novel hybrid image compression–encryption algorithm based on compressive sensing. Optik-Int J Light Electron Opt 125(18):5075–5080

    Article  Google Scholar 

  37. Zhou N, Zhang A, Zheng F, Gong L (2014) Novel image compression–encryption hybrid algorithm based on key-controlled measurement matrix in compressive sensing. Opt Laser Technol 62:152–160

    Article  Google Scholar 

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Authors and Affiliations

  1. Department of Electronics and Communication, SSN College of Engineering, Chennai, India

    K. Ashwini & R. Amutha

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  1. K. Ashwini
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  2. R. Amutha
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Correspondence to K. Ashwini.

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Ashwini, K., Amutha, R. Compressive sensing based simultaneous fusion and compression of multi-focus images using learned dictionary. Multimed Tools Appl 77, 25889–25904 (2018). https://doi.org/10.1007/s11042-018-5824-9

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  • DOI: https://doi.org/10.1007/s11042-018-5824-9

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