Skip to main content
SpringerLink
Log in

A multi-weight fusion framework for infrared and visible image fusion

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

Abstract

Infrared and visible image fusion (IVF) aims to generate a fused image with important thermal target and texture information from infrared and visible images. However, the existing advanced fusion methods have the problem of insufficient extraction of visible image details, and the fused image is not natural and does not conform to human visual perception. To solve this problem, we propose an effective infrared and visible image fusion framework inspired by the idea of multi-exposure fusion. First, we design an adaptive visible light exposure adjustment module to enhance the low-brightness pixel area information in the visible image to obtain an adaptive exposure image. Secondly, three feature weight maps of the input infrared, visible light and adaptive exposure images are extracted through the multi-weight feature extraction module: DSIFT map, saliency map and saturation map, and then the feature weight maps are optimized through the Mutually Guided Image Filtering (MuGIF). Then, we use the Gaussian and Laplacian pyramids to decompose and reconstruct the feature weight map and input image to obtain the pre-fused image. Finally, to further enhance the contrast of the pre-fused image, we use a Fast Guided Filter to enhance the pre-fused image to obtain the final fusion result. Qualitative and quantitative experiments show that the proposed method exhibits better fusion performance on public datasets compared with 11 state-of-the-art methods. In addition, this method can retain more visible image details, and the fusion result is more natural. Our code is publicly available at https://github.com/VCMHE/MWF_VIF.

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

Access this article

Log in via an institution

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
Algorithm 1
Fig. 6
Fig. 7
Fig. 8
Algorithm 2
Fig. 9
Fig. 10
Fig. 11
Fig. 12
Fig. 13
Fig. 14
Fig. 15
Fig. 16
Fig. 17
Fig. 18

Similar content being viewed by others

Data availability

The datasets generated during and analyzed during the current study are available at: https://github.com/VCMHE/MWF_VIF.

References

  1. Li J et al (2020) DRPL: deep regression pair learning for multi-focus image fusion. IEEE Trans Image Process 29:4816–4831. https://doi.org/10.1109/TIP.2020.2976190

    Article  Google Scholar 

  2. Li H, Zhao J, Li J, Yu Z, Lu G (2023) Feature dynamic alignment and refinement for infrared-visible image fusion: Translation robust fusion. Inf Fusion 95:26–41. https://doi.org/10.1016/j.inffus.2023.02.011

    Article  Google Scholar 

  3. Li J, Liang B, Lu X, Li M, Lu G, Xu Y (2023) From global to local: multi-patch and multi-scale contrastive similarity learning for unsupervised defocus blur detection. IEEE Trans Image Process 32:1158–1169. https://doi.org/10.1109/TIP.2023.3240856

    Article  Google Scholar 

  4. Zhou H et al (2020) Feature matching for remote sensing image registration via manifold regularization. IEEE J Sel Top Appl Earth Obs Remote Sens 13:4564–4574. https://doi.org/10.1109/JSTARS.2020.3015350

    Article  Google Scholar 

  5. Lin X et al (2022) Learning modal-invariant and temporal-memory for video-based visible-infrared person re-identification. In: IEEE/CVF Conference on Computer Vision and Pattern Recognition, CVPR 2022, New Orleans, LA, USA, June 18–24, 2022, IEEE, pp 20941–20950. https://doi.org/10.1109/CVPR52688.2022.02030

  6. Li S et al (2023) Logical relation inference and multiview information interaction for domain adaptation person re-identification. IEEE Trans Neural Netw Learn Syst 1–13. https://doi.org/10.1109/TNNLS.2023.3281504

  7. Zhou Y, Xie L, He K, Xu D, Tao D, Lin X (2023) Low-light image enhancement for infrared and visible image fusion. IET Image Proc 17(11):3216–3234

    Article  Google Scholar 

  8. Ma J et al (2020) Infrared and visible image fusion via detail preserving adversarial learning. Inf Fusion 54:85–98. https://doi.org/10.1016/j.inffus.2019.07.005

    Article  Google Scholar 

  9. Ma J, Ma Y, Li C (2019) Infrared and visible image fusion methods and applications: a survey. Inf Fusion 45:153–178. https://doi.org/10.1016/j.inffus.2018.02.004

    Article  Google Scholar 

  10. Li C et al (2023) Superpixel-based adaptive salient region analysis for infrared and visible image fusion. Neural Comput Appl 35:22511–22529

    Article  Google Scholar 

  11. Borsoi RA, Imbiriba T, Bermudez JCM (2020) Super-resolution for hyperspectral and multispectral image fusion accounting for seasonal spectral variability. IEEE Trans Image Process 29:116–127. https://doi.org/10.1109/TIP.2019.2928895

    Article  MathSciNet  Google Scholar 

  12. Wang J, Xi X, Li D, Li F (2023) FusionGRAM: an infrared and visible image fusion framework based on gradient residual and attention mechanism. IEEE Trans Instrum Meas 72:1–12. https://doi.org/10.1109/TIM.2023.3237814

    Article  Google Scholar 

  13. Li H, Wu X-J, Kittler J (2020) MDLatLRR: a novel decomposition method for infrared and visible image fusion. IEEE Trans Image Process 29:4733–4746. https://doi.org/10.1109/TIP.2020.2975984

    Article  Google Scholar 

  14. Peng Y, Lu B-L (2017) Robust structured sparse representation via half-quadratic optimization for face recognition. Multim Tools Appl 76(6):8859–8880. https://doi.org/10.1007/s11042-016-3510-3

    Article  Google Scholar 

  15. Liu G, Lin Z, Yan S, Sun J, Yu Y, Ma Y (2013) Robust recovery of subspace structures by low-rank representation. IEEE Trans Pattern Anal Mach Intell 35(1):171–184. https://doi.org/10.1109/TPAMI.2012.88

    Article  Google Scholar 

  16. Liu Y, Wang Z (2015) Simultaneous image fusion and denoising with adaptive sparse representation. IET Image Process 9(5):347–357. https://doi.org/10.1049/iet-ipr.2014.0311

    Article  Google Scholar 

  17. Li G, Lin Y, Qu X (2021) An infrared and visible image fusion method based on multi-scale transformation and norm optimization. Inf Fusion 71:109–129. https://doi.org/10.1016/j.inffus.2021.02.008

    Article  Google Scholar 

  18. Zhang Q, Wang F, Luo Y, Han J (2021) Exploring a unified low rank representation for multi-focus image fusion. Pattern Recognit 113:107752. https://doi.org/10.1016/j.patcog.2020.107752

    Article  Google Scholar 

  19. Ren L, Pan Z, Cao J, Liao J (2021) Infrared and visible image fusion based on variational auto-encoder and infrared feature compensation. Infrared Phys Technol 117:103839

    Article  Google Scholar 

  20. Qu L, Liu S, Wang M, Song Z (2022) TransMEF: A Transformer-Based Multi-Exposure Image Fusion Framework Using Self-Supervised Multi-Task Learning. In: Thirty-Sixth AAAI Conference on Artificial Intelligence, AAAI 2022, Thirty-Fourth Conference on Innovative Applications of Artificial Intelligence, IAAI 2022, The Twelveth Symposium on Educational Advances in Artificial Intelligence, EAAI 2022 Virtual Event, February 22 - March 1, 2022, AAAI Press, pp 2126–2134. https://doi.org/10.1609/aaai.v36i2.20109

  21. Zhao H, Nie R (2021) Dndt: Infrared and visible image fusion via densenet and dual-transformer. In: 2021 International Conference on Information Technology and Biomedical Engineering (ICITBE), IEEE, pp 71–75

  22. Qu L et al (2022) TransFuse: A Unified Transformer-based Image Fusion Framework using Self-supervised Learning. CoRR, vol. abs/2201.07451, [Online]. Available: https://arxiv.org/abs/2201.07451

  23. Xu H, Ma J, Jiang J, Guo X, Ling H (2022) U2Fusion: a unified unsupervised image fusion network. IEEE Trans Pattern Anal Mach Intell 44(1):502–518. https://doi.org/10.1109/TPAMI.2020.3012548

    Article  Google Scholar 

  24. Ma J, Tang L, Xu M, Zhang H, Xiao G (2021) STDFusionNet: an infrared and visible image fusion network based on salient target detection. IEEE Trans Instrum Meas 70:1–13. https://doi.org/10.1109/TIM.2021.3075747

    Article  Google Scholar 

  25. Ma J, Yu W, Liang P, Li C, Jiang J (2019) FusionGAN: a generative adversarial network for infrared and visible image fusion. Inf Fusion 48:11–26. https://doi.org/10.1016/j.inffus.2018.09.004

    Article  Google Scholar 

  26. Ma J, Xu H, Jiang J, Mei X, Steven Zhang X-P (2020) DDcGAN: a dual-discriminator conditional generative adversarial network for multi-resolution image fusion. IEEE Trans. Image Process 29:4980–4995. https://doi.org/10.1109/TIP.2020.2977573

    Article  Google Scholar 

  27. Ma J, Chen C, Li C, Huang J (2016) Infrared and visible image fusion via gradient transfer and total variation minimization. Inf Fusion 31:100–109. https://doi.org/10.1016/j.inffus.2016.02.001

    Article  Google Scholar 

  28. Zhang H, Ma J (2021) SDNet: A versatile squeeze-and-decomposition network for real-time image fusion. Int J Comput Vis 129:2761–2785. https://doi.org/10.1007/s11263-021-01501-8

    Article  Google Scholar 

  29. Ying Z, Li G, Gao W (2017) A Bio-Inspired Multi-Exposure Fusion Framework for Low-light Image Enhancement. CoRR abs/1711.00591 [Online]. Available: http://arxiv.org/abs/1711.00591

  30. Lowe DG (2004) Distinctive image features from scale-invariant keypoints. Int J Comput Vis 60(2):91–110. https://doi.org/10.1023/B:VISI.0000029664.99615.94

    Article  Google Scholar 

  31. Liu C, Yuen J, Torralba A (2011) SIFT Flow: dense Correspondence across scenes and its applications. IEEE Trans Pattern Anal Mach Intell 33(5):978–994. https://doi.org/10.1109/TPAMI.2010.147

    Article  Google Scholar 

  32. Zhang W, Cham W (2010) Gradient-directed composition of multi-exposure images. In: The Twenty-Third IEEE Conference on Computer Vision and Pattern Recognition, CVPR 2010, San Francisco, CA, USA, 13–18 June 2010, IEEE Computer Society, pp 530–536. https://doi.org/10.1109/CVPR.2010.5540168

  33. Guo X, Li Y, Ma J, Ling H (2020) Mutually guided image filtering. IEEE Trans Pattern Anal Mach Intell 42(3):694–707. https://doi.org/10.1109/TPAMI.2018.2883553

    Article  Google Scholar 

  34. Li Z, Zheng J, Rahardja S (2012) Detail-enhanced exposure fusion. IEEE Trans Image Process 21(11):4672–4676. https://doi.org/10.1109/TIP.2012.2207396

    Article  MathSciNet  Google Scholar 

  35. Xu L, Yan Q, Xia Y, Jia J (2012) Structure extraction from texture via relative total variation. ACM Trans Graph 31(6):139:1-139:10. https://doi.org/10.1145/2366145.2366158

    Article  Google Scholar 

  36. Guo X, Li Y, Ling H (2017) LIME: low-light image enhancement via illumination map estimation. IEEE Trans Image Process 26(2):982–993. https://doi.org/10.1109/TIP.2016.2639450

    Article  MathSciNet  Google Scholar 

  37. Hou X, Harel J, Koch C (2012) Image signature: highlighting sparse salient regions. IEEE Trans Pattern Anal Mach Intell 34(1):194–201. https://doi.org/10.1109/TPAMI.2011.146

    Article  Google Scholar 

  38. Mertens T, Kautz J, Reeth FV (2009) Exposure fusion: a simple and practical alternative to high dynamic range photography. Comput Graph Forum 28(1):161–171. https://doi.org/10.1111/j.1467-8659.2008.01171.x

    Article  Google Scholar 

  39. Ulucan O, Ulucan D, Türkan M (2023) Ghosting-free multi-exposure image fusion for static and dynamic scenes. Signal Process 202:108774. https://doi.org/10.1016/j.sigpro.2022.108774

    Article  Google Scholar 

  40. Bavirisetti DP, Xiao G, Liu G (2017) Multi-sensor image fusion based on fourth order partial differential equations. In: 20th International Conference on Information Fusion, FUSION 2017, Xi’an, China, July 10–13, 2017, IEEE, pp 1–9. https://doi.org/10.23919/ICIF.2017.8009719

  41. Bavirisetti DP, Xiao G, Zhao J, Dhuli R, Liu G (2019) Multi-scale guided image and video fusion: a fast and efficient approach. Circuits Syst Signal Process 38(12):5576–5605. https://doi.org/10.1007/s00034-019-01131-z

    Article  Google Scholar 

  42. Zhao Z, Xu S, Zhang C, Liu J, Zhang J (2020) Bayesian fusion for infrared and visible images. Signal Process 177:107734. https://doi.org/10.1016/j.sigpro.2020.107734

    Article  Google Scholar 

  43. Chen J, Li X, Luo L, Mei X, Ma J (2020) Infrared and visible image fusion based on target-enhanced multiscale transform decomposition. Inf Sci 508:64–78. https://doi.org/10.1016/j.ins.2019.08.066

    Article  Google Scholar 

  44. Li H, Wu X-J, Kittler J (2021) RFN-Nest: an end-to-end residual fusion network for infrared and visible images. Inf Fusion 73:72–86. https://doi.org/10.1016/j.inffus.2021.02.023

    Article  Google Scholar 

  45. Luo Y, He K, Xu D, Yin W, Liu W (2022) Infrared and visible image fusion based on visibility enhancement and hybrid multiscale decomposition. Optik 258:168914

    Article  Google Scholar 

  46. Park S, Vien AG, Lee C (2023) Cross-modal transformers for infrared and visible image fusion. IEEE Trans Circuits Syst Video Technol 1. https://doi.org/10.1109/TCSVT.2023.3289170

  47. Li H, Wu X-J, Durrani TS (2020) NestFuse: an infrared and visible image fusion architecture based on nest connection and spatial/channel attention models. IEEE Trans Instrum Meas 69(12):9645–9656. https://doi.org/10.1109/TIM.2020.3005230

    Article  Google Scholar 

  48. “TNO.” [Online]. Available: https://figshare.com/articles/TNO_Image_Fusion_Dataset/1008029

  49. Zhang X, Ye P, Xiao G (2020) VIFB: A Visible and Infrared Image Fusion Benchmark. In: 2020 IEEE/CVF Conference on Computer Vision and Pattern Recognition, CVPR Workshops 2020, Seattle, WA, USA, June 14–19, 2020, Computer Vision Foundation / IEEE, pp 468–478. https://doi.org/10.1109/CVPRW50498.2020.00060

  50. Li C, Liang X, Lu Y, Zhao N, Tang J (2019) RGB-T object tracking: benchmark and baseline. Pattern Recognit 96:106977. https://doi.org/10.1016/j.patcog.2019.106977

  51. Roberts W, van Aardt J, Ahmed F (2008) Assessment of image fusion procedures using entropy, image quality, and multispectral classification. J Appl Remote Sens 2:1–28. https://doi.org/10.1117/1.2945910

    Article  Google Scholar 

  52. Sheikh HR, Bovik AC (2006) Image information and visual quality. IEEE Trans Image Process 15(2):430–444. https://doi.org/10.1109/TIP.2005.859378

    Article  Google Scholar 

  53. Petrovic V, Xydeas C (2005) Objective image fusion performance characterization. In: Tenth IEEE International Conference on Computer Vision (ICCV’05) Volume 1, pp 1866–1871 Vol. 2. https://doi.org/10.1109/ICCV.2005.175

  54. Chen H, Varshney PK (2007) A human perception inspired quality metric for image fusion based on regional information. Inf Fusion 8(2):193–207. https://doi.org/10.1016/j.inffus.2005.10.001

    Article  Google Scholar 

Download references

Acknowledgements

This work was supported in part by the National Natural Science Foundation of China under Grant 62202416, Grant 62162068, Grant 62172354, Grant 62162065, in part by the Yunnan Province Ten Thousand Talents Program and Yunling Scholars Special Project under Grant YNWR-YLXZ-2018-022, in part by the Yunnan Provincial Science and Technology Department-Yunnan University “Double First Class” Construction Joint Fund Project under Grant No. 2019FY003012, in part by the Research Foundation of Yunnan Province No. 202105AF150011.

Author information

Authors and Affiliations

  1. School of Information Science and Engineering, Yunnan University, Kunming, 650091, China

    Yiqiao Zhou, Kangjian He, Dan Xu, Hongzhen Shi & Hao Zhang

Authors
  1. Yiqiao Zhou
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Kangjian He
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Dan Xu
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Hongzhen Shi
    View author publications

    You can also search for this author in PubMed Google Scholar

  5. Hao Zhang
    View author publications

    You can also search for this author in PubMed Google Scholar

Contributions

Yiqiao Zhou: Conceptualization, Methodology, Software, Writing – original draft. Hongzhen Shi: Visualization, Formal analysis. Hao Zhang: Validation, Data curation. Kangjian He: Supervision, Writing – review editing, Project administration, Funding acquisition. Dan Xu: Supervision, Project administration, Funding acquisition.

Corresponding author

Correspondence to Kangjian He.

Ethics declarations

Competing interest

The authors declare that there is no conflict of interest regarding the publication of the article.

Additional information

Publisher's Note

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

Rights and permissions

Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law.

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Zhou, Y., He, K., Xu, D. et al. A multi-weight fusion framework for infrared and visible image fusion. Multimed Tools Appl (2024). https://doi.org/10.1007/s11042-024-18141-y

Download citation

  • Received:

  • Revised:

  • Accepted:

  • Published:

  • DOI: https://doi.org/10.1007/s11042-024-18141-y

Keywords

Search

Navigation