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High-quality image multi-focus fusion to address ringing and blurring artifacts without loss of information

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Abstract

For many multi-focus image fusion methods, fused image visual and quantitative analysis is a challenging problem because of the presence of ringing and blurring artifacts. A high-quality image fusion framework to overcome the challenges of ringing and blurring artifacts in multi-focus image fusion while preserving the input image regions in the fused image proposed. The modified guided filtering proposed is called novel information preservation-based guided filtering (IPGF). Each input image has been decomposed, using IPGF, into a detail image (small-scale details) and base image (large-scale intensity variations) using the edge detection operator (EDO). The EDO has been used to generate the guidance image to overcome the loss of detail parts and smooth tiny details. Low-rank representation (LRR) was used to estimate the focus map and generate the detail parts fusion. The inherent property of removing artifacts using LRR helps to generate artifact-free detail image fusion. A simple but effective choose max-based fusion has been applied to fuse base images. The addition of base and detail images leads to the initial fusion result. The guided filtering was applied to a focus map, and then the outcome was used to generate the final fused image. Guided filtering is applied to suppress the ringing and blurring effects from the final fused image. The proposed method shows improved performance in terms of statistical, subjective, and objective analysis compared to recent methods.

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References

  1. Kong, L.B., Peng, X., Chen, Y., Wang, P., Xu, M.: Multi-sensor measurement and data fusion technology for manufacturing process monitoring: a literature review. Int. J. Extreme Manuf. (2020). https://doi.org/10.1088/2631-7990/ab7ae6

    Article  Google Scholar 

  2. Beddar-Wiesing, S., Bieshaar, M.: Multi-Sensor Data and Knowledge Fusion - A Proposal for a Terminology Definition. CoRR2001.04171 (2020)

  3. Kong, F., Zhou, Y., Chen, G.: Multimedia data fusion method based on wireless sensor network in intelligent transportation system. Multimed. Tools Appl. 79, 35195–35207 (2020). https://doi.org/10.1007/s11042-019-7614-4

    Article  Google Scholar 

  4. Gupta, K., Walia, G.S., Sharma, K.: Novel approach for multimodal feature fusion to generate cancelable biometric. Vis. Comput. (2020). https://doi.org/10.1007/s00371-020-01873-x

    Article  Google Scholar 

  5. Abdelbaky, A., Aly, S.: Two-stream spatiotemporal feature fusion for human action recognition. Vis. Comput. (2020). https://doi.org/10.1007/s00371-020-01940-3

    Article  Google Scholar 

  6. Cunha, A., Zhou, J., Do, M.: The nonsubsampled contourlet transform: theory, design, and applications. IEEE Trans. Image Process. Publ. IEEE Signal Process. Soci. 15, 3089–3101 (2006). https://doi.org/10.1109/TIP.2006.877507

    Article  Google Scholar 

  7. Jagtap, N.S., Thepade, S.: Systematic review and analysis of various multi-focus image fusion techniques. Int. J. Adv. Sci. Technol. 29(3), 3945–3959 (2020)

    Google Scholar 

  8. Arthur, F.G.M., Petrosian, A.: Wavelets in Signal and Image Analysis: From Theory to Practice. Springer, Berlin (2013)

    MATH  Google Scholar 

  9. Stathaki, T.: Image Fusion: Algorithms and Applications. Academic Press, Elsevier (2008)

    Google Scholar 

  10. Zhang, Q., Guo, B.: Multifocus image fusion using the nonsubsampled contourlet transform. Signal Process. 89(7), 1334–1346 (2009). https://doi.org/10.1016/j.sigpro.2009.01.012

    Article  MATH  Google Scholar 

  11. Nejati, M., Samavi, S., Shirani, S.: Multi-focus image fusion using dictionary-based sparse representation. Inf. Fusion 25, 72–84 (2015). https://doi.org/10.1016/j.inffus.2014.10.004

    Article  Google Scholar 

  12. Liu, Z., Chai, Y., Yin, H., Zhou, J., Zhu, Z.: A novel multi-focus image fusion approach based on image decomposition. Inf. Fusion 35, 102–116 (2017). https://doi.org/10.1016/j.inffus.2016.09.007

    Article  Google Scholar 

  13. Li, S., Kang, X., Fang, L., Hu, J., Yin, H.: Pixel-level image fusion: a survey of the state of the art. Inf. Fusion 33, 100–112 (2017). https://doi.org/10.1016/j.inffus.2016.05.004

    Article  Google Scholar 

  14. He, K., Sun, J., Tang, X.: Guided image filtering. IEEE Trans. Pattern Anal. Mach. Intell. 35(6), 1397–1409 (2013). https://doi.org/10.1109/tpami.2012.213

    Article  Google Scholar 

  15. Nejati, M., Samavi, S., Karimi, N., Reza Soroushmehr, S.M., Shirani, S., Roosta, I., Najarian, K.: Surface area-based focus criterion for multi-focus image fusion. Inf. Fusion 36, 284–295 (2017)

    Article  Google Scholar 

  16. Rahman, M.A., Liu, S., Wong, C.Y., Lin, S.C.F., Liu, S.C., Kwok, N.M.: Multifocal image fusion using degree of focus and Fuzzy logic. Digital Signal Process. 60, 1–19 (2017)

    Article  Google Scholar 

  17. Zhang, B.H., Lu, X.Q., Pei, H.Q., Liu, H., Zhao, Y., Zhou, W.T.: multi-focus image fusion algorithm based on focused region extraction. Neurocomputing 174, 733–748 (2017)

    Article  Google Scholar 

  18. Zhang, Y., Bai, X., Wang, T.: Boundary finding based multi-focus image fusion through multi-scale morphological focus-measure. Inf. Fusion 35, 81–101 (2017)

    Article  Google Scholar 

  19. Chen, Y.B., Guan, J.W., Cham, W.K.: Robust multi-focus image fusion using edge model and multi-matting. IEEE Trans. Image Process. 27(3), 1526–1541 (2018)

    Article  MathSciNet  MATH  Google Scholar 

  20. Du, C.B., Gao, S.: multi-focus image fusion algorithm based on pulse coupled neural networks and modified decision map. Optik 157, 1003–1015 (2018)

    Article  Google Scholar 

  21. Bouzos, O., Andreadis, I., Mitianoudis, N.: Conditional random field model for robust multi-focus image fusion. IEEE Trans. Image Process. 28, 1–1 (2019)

    Article  MathSciNet  MATH  Google Scholar 

  22. Yang, Y., Tong, S., Huang, S., Lin, P., Fang, Y.: A hybrid method for multi-focus image fusion based on fast discrete curvelet transform. IEEE Access 5, 14898–14913 (2017)

    Article  Google Scholar 

  23. Ma, J., Zhou, Z., Wang, B., Zong, H.: Infrared and visible image fusion based on visual saliency map and weighted least square optimization. Infrared Phys. Technol. 82, 8–17 (2017). https://doi.org/10.1016/j.infrared.2017.02.005

    Article  Google Scholar 

  24. Li, H., Wu, X.-J.: Multi-focus image fusion using dictionary learning and low-rank representation. Image Graph. (2017). https://doi.org/10.1007/978-3-319-71607-7_59

    Article  Google Scholar 

  25. Li, H., Wu, X.-J., Durrani, T.: Multi-focus Noisy Image Fusion using Low-Rank Representation (2018)

  26. He, K., Zhou, D., Zhang, X., Nie, R.: Multi-focus: focused region finding and multi-scale transform for image fusion. Neurocomputing 320, 157–170 (2018)

    Article  Google Scholar 

  27. Jiang, Q., Jin, X., Hou, J., Lee, S.-J., Yao, S.: Multi-sensor image fusion based on interval type-2 fuzzy sets and regional features in nonsubsampled shearlet transform domain. IEEE Sens. J. 18(6), 2494–2505 (2018)

    Article  Google Scholar 

  28. Yang, D., Hu, S., Liu, S., Ma, X., Sun, Y.: multi-focus image fusion based on block matching in 3D transform domain. J. Syst. Eng. Electron. 29(2), 415–428 (2018)

    Article  Google Scholar 

  29. Liu, S., Wang, J., Lu, Y., Hu, S., Ma, X., Wu, Y.: multi-focus image fusion based on residual network in non-subsampled shearlet domain. IEEE Access 7, 152043–152063 (2019)

    Article  Google Scholar 

  30. Guo, X., Nie, R., Cao, J., Zhou, D., Qian, W.: Fully convolutional network-based multifocus image fusion. Neural Comput. 30(7), 1775–1800 (2018)

    Article  MathSciNet  Google Scholar 

  31. Tang, H., Xiao, B., Li, W., Wang, G.: Pixel convolutional neural network for multi-focus image fusion. Inf. Sci. 433–434, 125–141 (2018)

    Article  MathSciNet  Google Scholar 

  32. Amin-Naji, M., Aghagolzadeh, A., Ezoji, M.: CNNs hard voting for multi-focus image fusion. J. Ambient Intell. Human. Comput. 11, 1749–1769 (2019)

    Article  Google Scholar 

  33. Amin-Naji, M., Aghagolzadeh, A., Ezoji, M.: Ensemble of CNN for multi-focus image fusion. Inf. Fusion 51, 204–214 (2019)

    Article  Google Scholar 

  34. Upla, K.P., Joshi, S., Joshi, M.V., Gajjar, P.P.: Multiresolution image fusion using edge-preserving filters. J. Appl. Remote Sens. 9(1), 096025 (2015). https://doi.org/10.1117/1.jrs.9.096025

    Article  Google Scholar 

  35. Wang, Z., Wang, S., Zhu, Y.: Multi-focus image fusion based on the improved PCNN and guided filter. Neural Process. Lett. 45(1), 75–94 (2016). https://doi.org/10.1007/s11063-016-9513-2

    Article  MathSciNet  Google Scholar 

  36. Na, Y., Zhao, L., Yang, Y., Ren, M.: Guided filter-based images fusion algorithm for CT and MRI medical images. IET Image Proc. 12(1), 138–148 (2018). https://doi.org/10.1049/iet-ipr.2016.0920

    Article  Google Scholar 

  37. Zhu, J., Jin, W., Li, L., Han, Z., Wang, X.: Multiscale infrared and visible image fusion using gradient domain guided image filtering. Infrared Phys. Technol. 89, 8–19 (2018). https://doi.org/10.1016/j.infrared.2017.12.003

    Article  Google Scholar 

  38. Jian, L., Yang, X., Zhou, Z., Zhou, K., Liu, K.: Multi-scale image fusion through rolling guidance filter. Futur. Gener. Comput. Syst. 83, 310–325 (2018). https://doi.org/10.1016/j.future.2018.01.039

    Article  Google Scholar 

  39. Zhang, Y., Wei, W., Yuan, Y.: Multi-focus image fusion with alternating guided filtering. SIViP (2018). https://doi.org/10.1007/s11760-018-1402-x

    Article  Google Scholar 

  40. Zhou, F., Li, X., Li, J., Wang, R., Tan, H.: Multifocus image fusion based on fast guided filter and focus pixels detection. IEEE Access 7, 50780–50796 (2019). https://doi.org/10.1109/access.2019.2909591

    Article  Google Scholar 

  41. Liu, Y., Dong, L., Ji, Y., Xu, W.: Infrared and visible image fusion through details preservation. Sensors 19(20), 4556 (2019). https://doi.org/10.3390/s19204556

    Article  Google Scholar 

  42. Qiu, X., Li, M., Zhang, L., Yuan, X.: Guided filter-based multi-focus image fusion through focus region detection. Signal Process. Image Commun. (2018). https://doi.org/10.1016/j.image.2018.12.004

    Article  Google Scholar 

  43. Geng, P., Liu, J.: An effective multifocus image fusion method using guided filter. Ind. Robot Int. J. Robot. Res. Appl. 1, 21 (2019). https://doi.org/10.1108/ir-05-2018-0097

    Article  Google Scholar 

  44. He, L., Yang, X., Lu, L., Wu, W., Ahmad, A., Jeon, G.: A novel multi-focus image fusion method for improving imaging systems by using cascade-forest model. EURASIP J. Image Video Process. (2020). https://doi.org/10.1186/s13640-020-0494-8

    Article  Google Scholar 

  45. Ch, M.M.I., Riaz, M.M., Iltaf, N., et al.: A multifocus image fusion using highlevel DWT components and guided filter. Multimed. Tools Appl. 79, 12817–12828 (2020). https://doi.org/10.1007/s11042-020-08661-8

    Article  Google Scholar 

  46. Vanmali, A.V., Kataria, T., Kelkar, S.G., Gadre, V.M.: Ringing artifacts in wavelet based image fusion: analysis, measurement and remedies. Inf. Fusion 56, 39–69 (2020). https://doi.org/10.1016/j.inffus.2019.10.003

    Article  Google Scholar 

  47. Bai, X., Zhang, Y., Zhou, F., Xue, B.: Quadtree-based multi-focus image fusion using a weighted focus measure. Inf. Fusion 22, 105–118 (2015). https://doi.org/10.1016/j.inffus.2014.05.003

    Article  Google Scholar 

  48. Liu, Y., Chen, X., Peng, H., Wang, Z.: Multi-focus image fusion with a deep convolutional neural network. Inf. Fusion 36, 191–207 (2017). https://doi.org/10.1016/j.inffus.2016.12.001

    Article  Google Scholar 

  49. http://www.quxiaobo.org/software/software_FusingImages.html

  50. http://www.imgfsr.com/sitebuilder/images

  51. http://mansournejati.ece.iut.ac.ir/content/lytro-multi-focus-dataset

  52. Xydeas, C.S., Petrovic, V.: Objective image fusion performance measure. Electron. Lett. 36(4), 308–309 (2000)

    Article  Google Scholar 

  53. Mahajan, H.B., Badarla, A., Junnarkar, A.A.: CL-IoT: cross-layer internet of things protocol for intelligent manufacturing of smart farming. J. Ambient Intell. Hum. Comput. 12, 7777–7791 (2021). https://doi.org/10.1007/s12652-020-02502-0

    Article  Google Scholar 

  54. Mahajan, H.B., Badarla, A.: Application of internet of things for smart precision farming: solutions and challenges. Int. J. Adv. Sci. Technol. 2018, 37–45 (2018)

    Google Scholar 

  55. Mahajan, H.B., Badarla, A.: Experimental analysis of recent clustering algorithms for wireless sensor network: application of IoT based smart precision farming. J. Adv. Res. Dyn. Control Syst. (2019). https://doi.org/10.5373/JARDCS/V11I9/20193162

    Article  Google Scholar 

  56. Mahajan, H.B., Badarla, A.: Detecting HTTP vulnerabilities in IoT-based precision farming connected with cloud environment using artificial intelligence. Int. J. Adv. Sci. Technol. 29(3), 214–226 (2020)

    Google Scholar 

  57. Mahajan, H.B., Badarla, A.: Cross-layer protocol for WSN-assisted IoT smart farming applications using nature inspired algorithm. Wirel. Pers. Commun. (2021). https://doi.org/10.1007/s11277-021-08866-6

    Article  Google Scholar 

  58. Li, J., Yang, B., Yang, W., Sun, C., Xu, J.: Subspace-based multi-view fusion for instance-level image retrieval. Vis. Comput. (2020). https://doi.org/10.1007/s00371-020-01828-2

    Article  Google Scholar 

  59. Asad, M., Yang, J., He, J., Shamsolmoali, P., He, X.: Multi-frame feature-fusion-based model for violence detection. Vis. Comput. (2020). https://doi.org/10.1007/s00371-020-01878-6

    Article  Google Scholar 

  60. Wang, C., He, C., Xu, M.: Fast exposure fusion of detail enhancement for brightest and darkest regions. Vis. Comput. (2021). https://doi.org/10.1007/s00371-021-02079-5

    Article  Google Scholar 

  61. Bhat, S., Koundal, D.: Multi-focus image fusion techniques: a survey. Artif. Intell. Rev. (2021). https://doi.org/10.1007/s10462-021-09961-7

    Article  Google Scholar 

  62. Aymaz, S., Köse, C., Aymaz, Ş: Multi-focus image fusion for different datasets with super-resolution using gradient-based new fusion rule. Multimed. Tools Appl. 79, 13311–13350 (2020). https://doi.org/10.1007/s11042-020-08670-7

    Article  Google Scholar 

  63. Kaur, H., Koundal, D., Kadyan, V.: Image fusion techniques: a survey. Arch. Comput. Methods Eng. (2021). https://doi.org/10.1007/s11831-021-09540-7

    Article  Google Scholar 

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  1. Pimpri Chinchwad College of Engineering, Pune, Maharashtra, India

    Nalini Santosh Jagtap & Sudeep D Thepade

  2. Dr D Y Patil Institute of Engineering Management and Research, SPPU, Pune, India

    Nalini Santosh Jagtap

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  1. Nalini Santosh Jagtap
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Jagtap, N.S., Thepade, S.D. High-quality image multi-focus fusion to address ringing and blurring artifacts without loss of information. Vis Comput 38, 4353–4371 (2022). https://doi.org/10.1007/s00371-021-02300-5

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