Abstract
In this study, we innovatively propose salience-combined map and gradientlet filter for infrared and visible image fusion. It can enhance the infrared image of the target and also retain more detailed textures. First, our method is based on a multi-scale decomposition framework and gradientlet filter to decompose the source graph into approximate layers and residual layers. The approximate layers preserve smooth areas of the source images without edge blurring. The residual layers reflect the small gradients and noise of the source image. Since the texture part of the residual layer is weak, we introduce a Gamma-enhanced gradient map to complement the texture. The initial fusion image can be obtained by fusing the approximate layers and the residual layers. The salience-combined map directly extracts salient objects from infrared images according to pixel threshold segmentation, and extracts background information other than objects from visible images. Then the salience-combined map is used to guide the initial fusion image to get the final image. In our qualitative analysis, we compared our method against 5 traditional methods and deep learning-based methods. In the quantitative assessment, utilizing 29 pairs of randomly selected source images, our algorithm distinctly showcased absolute superiority across various metrics, including EN, SF, AG, and FD. The aforementioned results affirm that our method ensures the generation of fused images with clear targets and rich details.
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Data Availability
All public images used in this paper are sourced from the following datasets:
1. TNO dataset: https://doi.org/10.6084/m9.figshare.1008029.v1
2. RoadScene dataset: https://github.com/frostcza/RoadScene
References
Li H, Wu X (2019) Densefuse: A fusion approach to infrared and visible images. IEEE Trans Image Process 28(5):2614–2623
Peter J, Edward H (1987) The laplacian pyramid as a compact image code. In: Oscar F (ed) Martin A. Readings in Computer Vision, Morgan Kaufmann, San Francisco (CA) pp, pp 671–679
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
Ma J, Zhang H, Shao Z, Liang P, Xu H (2020) Ganmcc: A generative adversarial network with multiclassification constraints for infrared and visible image fusion. IEEE Trans Instrum Meas 70:1–14
Du J, Li W, Lu K, Xiao B (2016) An overview of multi-modal medical image fusion. Neurocomputing 215:3–20
Fernandez B, Haut J, Paoletti M, Plaza J, Plaza A, Pla F (2018) Remote sensing image fusion using hierarchical multimodal probabilistic latent semantic analysis. IEEE J Selected Topics Applied Earth Obser Remote Sensing 11(12):4982–4993
Li H, Li X, Yu Z, Mao C (2016) Multifocus image fusion by combining with mixed-order structure tensors and multiscale neighborhood. Inf Sci 349:25–49
Zhou Z, Wang B, Li S, Dong M (2016) Perceptual fusion of infrared and visible images through a hybrid multi-scale decomposition with gaussian and bilateral filters. Information Fusion 30:15–26
Liu Y, Chen X, Cheng J, Peng H, Wang Z (2018) Infrared and visible image fusion with convolutional neural networks. Int J Wavelets Multiresolut Inf Process 16(03):1850018
Dogra A, Goyal B, Agrawal S (2017) From multi-scale decomposition to non-multi-scale decomposition methods: a comprehensive survey of image fusion techniques and its applications. IEEE Access 5:16040–16067
Tang L, Yuan J, Ma J (2022) Image fusion in the loop of high-level vision tasks: A semantic-aware real-time infrared and visible image fusion network. Information Fusion 82:28–42
Zhang H, Ma J (2021) Sdnet: A versatile squeeze-and-decomposition network for real-time image fusion. Int J Comput Vision 129(10):2761–2785
Ma J, Xu H, Jiang J, Mei X, Zhang X (2020) Ddcgan: A dual-discriminator conditional generative adversarial network for multi-resolution image fusion. IEEE Trans Image Process 29:4980–4995
Liu Y, Liu S, Wang Z (2015) A general framework for image fusion based on multi-scale transform and sparse representation. Information fusion 24:147–164
Li S, Kang X, Hu J (2013) Image fusion with guided filtering. IEEE Trans Image Process 22(7):2864–2875
Gupta M, Kumar N, Gupta N, Zaguia A (2022) Fusion of multi-modality biomedical images using deep neural networks. Soft Comput 26(16):8025–8036
Bulanon DM, Burks TF, Alchanatis V (2009) Image fusion of visible and thermal images for fruit detection. Biosys Eng 103(1):12–22
Chipman L, Orr T, Graham L (1995) Wavelets and image fusion. In Proceedings international conference on image processing, vol 3, IEEE pp 248–251
Saeedi J, Faez K (2012) Infrared and visible image fusion using fuzzy logic and population-based optimization. Appl Soft Comput 12(3):1041–1054
Zou Y, Liang X, Wang T (2013) Visible and infrared image fusion using the lifting wavelet. TELKOMNIKA Indonesian J Electr Eng 11(11):6290–6295
Yan X, Qin H, Li J, Zhou H, Zong J (2015) Infrared and visible image fusion with spectral graph wavelet transform. JOSA A 32(9):1643–1652
Xu L, Du J, Zhang Z (2015) Infrared-visible video fusion based on motion-compensated wavelet transforms. IET Image Proc 9(4):318–328
Do MN, Vetterli M (2005) The contourlet transform: an efficient directional multiresolution image representation. IEEE Trans Image Process 14(12):2091–2106
Li H, Liu L, Huang W, Yue C (2016) An improved fusion algorithm for infrared and visible images based on multi-scale transform. Infrared Physics & Technology 74:28–37
Da C, Arthur L, Zhou J, Do MN (2006) The nonsubsampled contourlet transform: theory, design, and applications. IEEE Trans Image Process 15(10):3089–3101
Farbman Z, Fattal R, Lischinski D, Szeliski R (2008) Edge-preserving decompositions for multi-scale tone and detail manipulation. ACM Trans Graphics (TOG) 27(3):1–10
Cui G, Feng H, Xu Z, Li Q, Chen Y (2015) Detail preserved fusion of visible and infrared images using regional saliency extraction and multi-scale image decomposition. Optics Communications 341:199–209
He K, Sun J, Tang X (2012) Guided image filtering. IEEE Trans Pattern Anal Mach Intell 35(6):1397–1409
Zhou Z, Wang B, Li S, Dong M (2016) Perceptual fusion of infrared and visible images through a hybrid multi-scale decomposition with gaussian and bilateral filters. Information Fusion 30:15–26
Cai X, Zhao W, Gao F (2010) Image fusion algorithm based on adaptive pulse coupled neural networks in curvelet domain. In IEEE 10th International conference on signal processing proceedings, IEEE pp 845–848
Bhatnagar G, Wu Q (2012) An image fusion framework based on human visual system in framelet domain. Int J Wavelets Multiresolut Inf Process 10(01):1250002
Geng P, Wang Z, Zhang Z, Xiao Z (2012) Image fusion by pulse couple neural network with shearlet. Opt Eng 51(6):067005
Naidu VPS (2013) Novel image fusion techniques using dct. Intern J Comput Sci Business Inform 5(1):1–18
Song Y, Xiao J, Yang J, Chai Z, Wu Y (2016) Research on mr-svd based visual and infrared image fusion. In Infrared technology and applications, and robot sensing and advanced control, vol 10157, International society for optics and photonics pp 101571C
Candes E, Demanet L, Donoho D, Ying L (2006) Fast discrete curvelet transforms. Multiscale Modeling & Simulation 5(3):861–899
Ma J, Zhou Y (2020) Infrared and visible image fusion via gradientlet filter. Comput Vis Image Underst 197:103016
Toet A (2014). Tno image fusion dataset URL. https://doi.org/10.6084/m9.figshare.1008029.v1
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
Nencini F, Garzelli A, Baronti S, Alparone L (2007) Remote sensing image fusion using the curvelet transform. Information fusion 8(2):143–56
Ma J, Yu W, Liang P, Li C, Jiang J (2019) Fusiongan: A generative adversarial network for infrared and visible image fusion. Information Fusion 48:11–26
Zhang Y, Liu Y, Sun P, Yan H, Zhao X, Zhang L (2020) Ifcnn: A general image fusion framework based on convolutional neural network. Information Fusion 54:99–118
Cui G, Feng H, Xu Z, Li Q, Chen Y (2015) Detail preserved fusion of visible and infrared images using regional saliency extraction and multi-scale image decomposition. Optics Communications 341:199–209
Wesley RJ, Jan A, Van A, Fethi BA (2008) Assessment of image fusion procedures using entropy, image quality, and multispectral lassification. J Appl Remote Sens 2(1):1–28
Eskicioglu AM, Fisher PS (1995) Image quality measures and their performance. IEEE Trans Commun 43(12):2959–2965
Ma J, Ma Y, Li C (2019) Infrared and visible image fusion methods and applications: A survey. Information Fusion pp 153–178
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This work was supported by the National Natural Science Foundation of China nos. 62073304 and 62373338.
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Jun, C., Lei, C., Wei, L. et al. Infrared and visible image fusion via gradientlet filter and salience-combined map. Multimed Tools Appl (2023). https://doi.org/10.1007/s11042-023-17778-5
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DOI: https://doi.org/10.1007/s11042-023-17778-5