Nothing Special   »   [go: up one dir, main page]
Skip to main content

Advertisement

Springer Nature Link
Log in

Attention based dual path fusion networks for multi-focus image

  • Published:
Multimedia Tools and Applications Aims and scope Submit manuscript

Abstract

An important goal of multi-focus image fusion technology is to generate all-focus images that can better retain the source image information, while improving the quality and performance of image fusion. However, traditional image fusion methods usually have problems such as block artifacts, artificial edges, halo effects, and decreased contrast. To solve these problems, this paper proposes a dual-path fusion network (A-DPFN) with attention mechanism for multi-focus image fusion. Firstly, our method splits the complete image into image blocks, and obtains higher image classification with the preprocessing of the image blocks, so that our proposed dual-path fusion network accelerates the model convergence speed; Secondly, feature extraction block1 (FEB1) and feature extraction block2 (FEB2) in our network respectively extract the feature information of pair of focused images, in which we have added an attention mechanism; Finally, we merge the obtained pair of feature images as the input of the feature fusion block (FFB), and enhance the details of the image through the down-sampling block (DB) and the up-sampling block (UB). The experimental results show that the method has strong robustness and can effectively avoid problems such as block effect and artificial effect. Compared with the traditional image fusion method, the multi-focus image fusion method proposed in this paper is more effective.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Log in via an institution

Subscribe and save

Springer+ Basic
$34.99 /Month
  • Get 10 units per month
  • Download Article/Chapter or eBook
  • 1 Unit = 1 Article or 1 Chapter
  • Cancel anytime
Subscribe now

Buy Now

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5
Fig. 6
Fig. 7
Fig. 8
Fig. 9
Fig. 10
Fig. 11

Similar content being viewed by others

References

  1. Amin-Naji M, Aghagolzadeh A, Ezoji M (2019) Ensemble of CNN for multi-focus image fusion. Inform Fusion 51(February):201–214

    Article  Google Scholar 

  2. Aymaz S, Köse C, Aymaz S (2020) Multi-focus image fusion for different datasets with super-resolution using gradient-based new fusion rule. Multimedia Tools and Applications, pp 1–40

  3. Amin-Naji M, Ranjbar-Noiey P, Aghagolzadeh A (2017) Multi-focus image fusion using Singular Value Decomposition in DCT domain. In: 2017 10th Iranian conference on machine vision and image processing (MVIP), pp 45–51

  4. Amin-Naji M, Ranjbar-Noiey P, Aghagolzadeh A (2018) Multi-focus image fusion using singular value decomposition in DCT domain. In: 2017 10th Iranian conference on machine vision and image processing (MVIP)

  5. Bavirisetti DP, Xiao G, Zhao J, Dhuli R, Liu G (2019) Multi-scale guided image and video fusion: A fast and efficient approach. Circuits, Systems, and Signal Processing 38(12):5576–5605

    Article  Google Scholar 

  6. Bavirisetti DP, Xiao G, Zhao J, Dhuli R, Liu G (2019) Multi-scale guided image and video fusion: A fast and efficient approach. Circuits, Systems, and Signal Processing 38:5576–5605

    Article  Google Scholar 

  7. Chakraborty C, Gupta B, Ghosh SK, Das DK, Chakraborty C (2016) Telemedicine supported chronic wound tissue prediction using classification approaches. J Med Syst 40:68

    Article  Google Scholar 

  8. Chen Y, Blum RS (2009) A new automated quality assessment algorithm for image fusion. Image Vis Comput 27:1421–1432

    Article  Google Scholar 

  9. Cvejic N, Canagarajah CN, Bull DR (2006) Image fusion metric based on mutual information and Tsallis entropy. Electron Lett 42:626–627

    Article  Google Scholar 

  10. Du C, Gao S (2017) Image Segmentation-Based Multi-Focus Image Fusion Through Multi-Scale Convolutional Neural Network. IEEE Access 5:15750–15761

    Article  Google Scholar 

  11. Gai D, Shen X, Chen H, Su P (2020) Multi-focus image fusion method based on two stage of convolutional neural network. Signal Process 176:107681

    Article  Google Scholar 

  12. Guo Y, Huang C, Zhang Y, Li Y, Chen W (2020) A novel multitemporal image-fusion algorithm: Method and application to GOCI and himawari images for inland water remote sensing. IEEE Trans Geosci Remote Sens 58:4018–4032. 2020-01-01

    Article  Google Scholar 

  13. Guo X, Nie R, Cao J, Zhou D, Mei L, He K (2019) FuseGAN: Learning to fuse multi-focus image via conditional generative adversarial network. IEEE Transactions on Multimedia 21:1982–1996

    Article  Google Scholar 

  14. Guo R, Shen X, Dong X, Zhang X (2020) Multi-focus image fusion based on fully convolutional networks. Frontiers of Information Technology and Electronic Engineering 21:1019–1033. 2020-01-01

    Article  Google Scholar 

  15. Hong R, Wang C, Ge Y, Wang M, Wu X (2007) Salience preserving mufti-focus image fusion. In: 2007 IEEE international conference on multimedia and expo, pp 1663–1666

  16. Hu X, Yang K, Fei L, Wang K (2019) Acnet: Attention based network to exploit complementary features for rgbd semantic segmentation. In: 2019 IEEE international conference on image processing (ICIP), IEEE, pp 1440–1444

  17. Kingma D, Ba J (2014) Adam: A method for stochastic optimization computer science - learning

  18. Krishnan MMR, Banerjee S, Chakraborty C, Chakraborty C, Ray AK (2010) Statistical analysis of mammographic features and its classification using support vector machine. Expert Syst Appl 37:470–478

    Article  Google Scholar 

  19. Liu Y, Chen X, Ward RK, Wang ZJ (2016) Image fusion with convolutional sparse representation. IEEE Signal Process Lett 23(12):1882–1886

    Article  Google Scholar 

  20. Li G, Li L, Zhu H, Liu X, Jiao L (2019) Adaptive multiscale deep fusion residual network for remote sensing image classification. IEEE Trans Geosci Remote Sens 57:8506–8521. 2019-01-01

    Article  Google Scholar 

  21. Li S, Kang X, Hu J, Yang B (2013) Image matting for fusion of multi-focus images in dynamic scenes. Inform Fusion 14(2):147–162

    Article  Google Scholar 

  22. Li S, Kang X, Hu J (2013) Image fusion with guided filtering. IEEE Trans Image Process 22:2864–2875

    Article  Google Scholar 

  23. Liu Y, Chen X, Ward R, Wang ZJ (2016) Image fusion with convolutional sparse representation. IEEE Signal Process Lett 23:1882–1886

    Article  Google Scholar 

  24. Li J, Guo X, Lu G, Zhang B, Zhang D (2020) DRPL: Deep regression pair learning for Multi-Focus image fusion. IEEE Trans Image 29:4816–4831

    Article  Google Scholar 

  25. Liu Y, Chen X, Peng H, Wang Z (2017) Multi-focus image fusion with a deep convolutional neural network. Inform Fusion 36:191–207

    Article  Google Scholar 

  26. Long J, Shelhamer E, Darrell T (2015) Fully convolutional networks for semantic segmentation. In: Proceedings of the IEEE conference on computer vision and pattern recognition, pp 3431–3440

  27. Lai R, Li Y, Guan J, Xiong A (2019) Multi-scale visual attention deep convolutional neural network for multi-focus image fusion. IEEE Access 7:114385–114399

    Article  Google Scholar 

  28. Liu Z, Blasch E, Xue Z, Zhao J, Laganiere R, Wu W (2012) Objective assessment of multiresolution image fusion algorithms for context enhancement in night vision: A comparative study. IEEE Trans Pattern Anal Mach Intell 34:94–109

    Article  Google Scholar 

  29. Liu Z, Blasch E, Xue Z, Zhao J, Laganiere R, Wu W (2012) Objective assessment of multiresolution image fusion algorithms for context enhancement in night vision: A comparative study. IEEE Trans Pattern Anal Mach Intell 34:94–109

    Article  Google Scholar 

  30. Liu Z, Forsyth DS, Laganière R (2008) A feature-based metric for the quantitative evaluation of pixel-level image fusion. Comput Vis Image Underst 109:56–68

    Article  Google Scholar 

  31. Liu Y, Wang Z (2014) Simultaneous image fusion and denoising with adaptive sparse representation. IET Image Process 9(5):347–357

    Article  Google Scholar 

  32. Ma B, Ban X, Huang H, Zhu Y (2019) Sesf-fuse: An unsupervised deep model for multi-focus image fusion. arXiv:1908.01703

  33. Nejati M, Samavi S, Shirani S (2015) Multi-focus image fusion using dictionary-based sparse representation. Inform Fusion 25:72–84

    Article  Google Scholar 

  34. Paul S, Sevcenco IS, Agathoklis P (2016) Multi-Exposure and Multi-Focus Image Fusion in Gradient Domain. Journal of Circuits, Systems and Computers 25:1650123.1–1650123.18

    Article  Google Scholar 

  35. Paul S, Sevcenco IS, Agathoklis P (2016) Multiexposure and multi-focus image fusion in gradient domain. Journal of Circuits, Systems and Computers 25(10):1650123

    Article  Google Scholar 

  36. Prabhakar KRSV (2017) DeepFuse: A deep unsupervised approach for exposure fusion with extreme exposure image pairs. In: Proceedings of the IEEE international conference on computer vision (ICCV), pp 4724–4732

  37. Qu G, Zhang D, Yan P (2002) Information measure for performance of image fusion. Electron Lett 38:313–315

    Article  Google Scholar 

  38. Sarkar A, Khan MZ, Singh MM, Noorwali A, Chakraborty C, Pani SK (2021) Artificial neural synchronization using nature inspired whale optimization. IEEE Access 9:16435–16447

    Article  Google Scholar 

  39. SH, MX, ZL (2016) An integrated framework for the spatio–temporal–spectral fusion of remote sensing images. IEEE Transactions On Geoscience and Remote Sensing 54:7135–7148. 2016-01-01

    Article  Google Scholar 

  40. Sarker MK, Rashwan H, Akram F, Talavera E (2019) Recognizing food places in egocentric Photo-Streams using Multi-Scale atrous convolutional networks and Self-Attention mechanism. IEEE Access 7:39069–39082

    Article  Google Scholar 

  41. Song X, Wu X-J (2018) Multi-focus image fusion with PCA filters of PCANet. In: IAPR workshop on multimodal pattern recognition of social signals in humancomputer interaction, Springer, pp 1–17

  42. Savi S, Babi Z (2012) Multifocus image fusion based on empirical mode decomposition. In: 19th IEEE In- ternational conference on systems, signals and image processing (IWSSIP)

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

    Article  MathSciNet  Google Scholar 

  44. Tian J, Chen L, Ma L, Yu W (2011) Multi-focus image fusion using a bilateral gradient-based sharpness criterion. Opt Commun 284(1):80–87

    Article  Google Scholar 

  45. Vaswani A, Shazeer N, Parmar N, Uszkoreit J, Jones L, Gomez AN, Kaiser L, Polosukhin I (2017) Attention is all you need. arXiv:1706.03762

  46. Wang Q, Shen Y, Jin J (2008) Performance evaluation of image fusion techniques. Image Fusion, pp 469–492

  47. Xu T, Zhang P, Huang Q, Zhang H (2018) AttnGAN: Fine-Grained text to image generation with attentional generative adversarial networks. In: 2018 IEEE/CVF conference on computer vision and pattern recognition (CVPR)

  48. Xiang K, Yang K, Wang K (2021) Polarization-driven semantic segmentation via efficient attention-bridged fusion. Opt Express 29:4802–4820

    Article  Google Scholar 

  49. Xu S, Wei X, Zhang C, Liu J, Zhang J (2020) Mffw: A new dataset for multi-focus image fusion. arXiv:2002.04780

  50. Yang Z, He X, Gao J, Deng L, Smola A (2016) Stacked attention networks for image question answering. In: 2016 IEEE conference on computer vision and pattern recognition (CVPR), pp 1063–6919

  51. Zhang Q, Levine MD (2016) Robust multi-focus image fusion using multi-task sparse representation and spatial context. IEEE Trans Image Process 25:2045–2058

    Article  MathSciNet  Google Scholar 

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

    Article  Google Scholar 

  53. Zhang Y, Liu Y, Sun P, Yan H, Zhao X, Zhang L (2020) IFCNN: A general image fusion framework based on convolutional neural network. Inform Fusion 54:99–118

    Article  Google Scholar 

  54. Zhang Y, Liu Y, Sun P, Yan H, Zhao X, Zhang L (2020) IFCNN: A general image fusion framework based on convolutional neural network. Inform Fusion 54:99–118

    Article  Google Scholar 

  55. Zhao J, Laganiere R, Liu Z (2006) Performance assessment of combinative pixel-level image fusion based on an absolute feature measurement. International Journal of Innovative Computing Information and Control Ijicic, vol 3

  56. Zhu J, Park T, Isola P, Efros AA (2017) Unpaired image-to-image translation using cycle-consistent adversarial networks. In: Proceedings of the IEEE international conference on computer vision, pp 2223–2232

Download references

Acknowledgements

The authors acknowledge the National Natural Science Foundation of China (Grant nos. 61772319, 619-76125), and Shandong Natural Science Foundation of China (Grant no. ZR2017MF049).

Author information

Authors and Affiliations

  1. School of Information and electronic engineering, Shandong Technology and Business University, Yantai, 264005, China

    Nana Yu & Zhen Hua

  2. School of Computer Science and Technology, Shandong Technology and Business University, Yantai, 264005, China

    Jinjiang Li

Authors
  1. Nana Yu
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Jinjiang Li
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Zhen Hua
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Jinjiang Li.

Additional information

Publisher’s note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Yu, N., Li, J. & Hua, Z. Attention based dual path fusion networks for multi-focus image. Multimed Tools Appl 81, 10883–10906 (2022). https://doi.org/10.1007/s11042-022-12046-4

Download citation

  • Received:

  • Revised:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s11042-022-12046-4

Keywords

Search

Navigation