Abstract
Multi-source image fusion has become an important and useful new technology in the image understanding and computer vision fields. The purpose of multi-source image fusion is to intelligently synthesize image data from multiple information sources, to generate more accurate and reliable descriptions and judgments than single-sensor data and to make fused images more consistent with human and machine visual features. Although there are many studies on multi-source image fusion, few papers summarize both theoretical and experimental aspects. This paper reviews, classifies and discusses the more advanced multi-source image fusion methods. We comprehensively introduce existing image fusion evaluation methods and compare them based on different standards. The representative algorithms are evaluated by using 12 famous target fusion metrics, and the advantages and disadvantages of each type are discussed in detail. Through research, the challenges encountered in this field and possible future research directions and development prospects are discussed.
Similar content being viewed by others
Data Availability
The datasets analysed during the current study are https://github.com/yuliu316316/MFIF, https://github.com/hli1221/imagefusion_deeplearning and https://github.com/hanna-xu/FusionDN.
References
Zhang XC (2021) Deep Learning-based Multi-focus Image Fusion: A Survey and A Comparative Study. IEEE Trans on Pattern Analysis and Machine Intelligence. https://doi.org/10.1109/TPAMI.2021.3078906
Shao ZF, Cai JJ (2018) Remote Sensing Image Fusion With Deep Convolutional Neural Network. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing 1:1656–1669. https://doi.org/10.1109/JSTARS.2018.2805923
Yin M, Liu XN, Liu Y (2019) Medical Image Fusion With Parameter-Adaptive Pulse Coupled Neural Network in Nonsubsampled Shearlet Transform Domain. IEEE Trans on instrumentation and measurement 68(1):49–64. https://doi.org/10.1109/TIM.2018.2838778
Kanika B, Deepika K, Bhisham S, Yu-Chen Hu and Atef Z (2022) A fuzzy convolutional neural network for enhancing multi-focus image fusion. Visual Communication and Image Representation 84. https://doi.org/10.1016/j.jvcir.2022.103485
Ma J, Ma Y and Li C (2019) Infrared and visible image fusion methods and applications: A survey.Information Fusion, 45:53–178. https://doi.org/10.1016/j.inffus.2018.02.004
Deng X, Zhang YT, Xu M, Gu SH, DuanYP, (2021) Deep Coupled Feedback Network for Joint Exposure Fusion and Image Super-Resolution. IEEE Trans on Image Processing 30:3098–3112. https://doi.org/10.1109/TIP.2021.3058764
Stathaki T (2008) Image Fusion: Algorithms and Applications. Academic Press
Liu Y, Wang Z (2015) Dense sift for ghost-free multi-exposure fusion. Journal of Visual Communication and Image Representation. 31:208–224. https://doi.org/10.1016/j.jvcir.2015.06.021
Liu W, Wang Z (2020) A novel multi-focus image fusion method using multiscale shearing non-local guided averaging filter. Signal Processing 166:107252. https://doi.org/10.1016/j.sigpro.2019.107252
Amin-Naji M, Aghagolzadeh A (2018) Multi-focus image fusion in DCT domain using variance and energy of Laplacian and correlation coefficient for visual sensor networks. Journal of AI and Data Mining 6(2):233–250. https://doi.org/10.22044/JADM.2017.5169.1624
Liu Y, Wang Z (2013) Multi-focus image fusion based on wavelet transform and adaptive block. Journal of Image and Graphics 18(11):1435–1444
Bavirisetti D and Dhuli R (2018) Multi-focus image fusion using multiscale image decomposition and saliency detection.Ain Shams Engineering Journal, 9 (4):1103–1117. https://doi.org/10.1016/j.asej.2016.06.011
Liu Y, Liu S and Wang Z (2015)A general framework for image fusion based on multi-scale transform and sparse representation. Information Fusion, 24:147–164. https://doi.org/10.1016/j.inffus.2014.09.004
Zhou Z, Li S, Wang B (2014) Multi-scale weighted gradient based fusion for multi-focus images. Information Fusion 20:60–72. https://doi.org/10.1016/j.inffus.2013.11.005
Zhang Y, Bai X, andWang T, (2017) Boundary finding based multi-focus image fusion through multi-scale morphological focus-measure. Information fusion 35:81–101. https://doi.org/10.1016/j.inffus.2016.09.006
Tian J, Chen L, Ma L, Yu W (2011) Multi-focus image fusion using a bilateral gradient-based sharpness criterion. Optics communications 284(1):80–87. https://doi.org/10.1016/j.optcom.2010.08.085
Shreyamsha Kumar BK (2015) Image fusion based on pixel significance using cross bilateral filter. Signal, Image and Video Processing 9(5):1193–1204
Liu Y, Liu SP, Wang ZF (2015) A general framework for image fusion based on multi-scale transform and sparse representation. Information Fusion 24(1):147–164. https://doi.org/10.1016/j.inffus.2014.09.004
Li S, Kang X, Hu J (2013) Image fusion with guided filtering. IEEE Trans on Image Processing 22(7):2864–2875. https://doi.org/10.1109/TIP.2013.2244222
Li S, Kang X, Hu J, Yang B (2013) Image matting for fusion of multi-focus images in dynamic scenes. Information Fusion 14(2):147–162. https://doi.org/10.1016/j.inffus.2011.07.001
Amin-Naji M, Aghagolzadeh A, Ezoji M (2019) Ensemble of CNN for Multi-Focus Image Fusion. Information Fusion 51:201–214. https://doi.org/10.1016/j.inffus.2019.02.003
Xu H, Fan F, Zhang H, Le Z, Huang J (2020) A deep model for multi-focus image fusion based on gradients and connected regions. IEEE Access 8:316–327. https://doi.org/10.1109/ACCESS.2020.2971137
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(114):385–399. https://doi.org/10.1109/ACCESS.2019.2935006
Zhang H, Le Z, Shao Z, Xu H, Ma J (2021) MFF-GAN: An unsupervised generative adversarial network with adaptive and gradient joint constraints for multi-focus image fusion. Information Fusion 66:40–53. https://doi.org/10.1016/j.inffus.2020.08.022
Song X and Wu XJ (2019) Multi-focus Image Fusion with PCA Filters of PCANet. in IAPR Workshop on Multimodal Pattern Recognition of Social Signals in Human-Computer Interaction. Springer,1–17
Wang Q, Chen W, Wu X, Li Z (2019) Detail-enhanced multi-scale exposure fusion in yuv color space. IEEE Trans. Circuits Syst. Video Technol. 26(3):1243–1252. https://doi.org/10.1109/TCSVT.2019.2919310
Yang Y, Cao W, Wu S, Li Z (2018) Multi-scale fusion of two large-exposure-ratio images. IEEE Signal Process. Lett. 25(12):1885–1889. https://doi.org/10.1109/LSP.2018.2877893
Liu Y and Wang Z (2014) Simultaneous image fusion and denoising with adaptive sparse representation. Image Process. Iet 9, 347–357. https://doi.org/10.1049/iet-ipr.2014.0311(2014)
Li H, Ma K, Yong H, Zhang L (2020) Fast multi-scale structural patch decomposition for multi-exposure image fusion. IEEE Trans. Image Process. 29:5805–5816. https://doi.org/10.1109/TIP.2020.2987133
Lee SH, Park JS and Cho NI (2018) A multi-exposure image fusion based on the adaptive weights reflecting the relative pixel intensity and global gradient// Proceedings of the 2018 25th IEEE International Conference on Image Processing (ICIP), IEEE:1737–1741. https://doi.org/10.1109/ICIP.2018.8451153
Hayat N, Imran M (2019) Ghost-free multi exposure image fusion technique using dense sift descriptor and guided filter. Journal of Visual Communication and Image Representation. 62:295–308. https://doi.org/10.1016/j.jvcir.2019.06.002
Zhang H, Xu H, Xiao Y, Guo X and Ma J (2020) Rethinking the image fusion: a fast unified image fusion network based on proportional maintenance of gradient and intensity, in: Proceedings of the AAAI Conference on Artificial Intelligence.12797–12804
Prabhakar KR, Srikar VS and Babu RV (2017) DeepFuse: A Deep Unsupervised Approach for Exposure Fusion with Extreme Exposure Image Pairs//Proceedings of the 2017 IEEE International Conference on Computer Vision (ICCV).IEEE Computer Society, 4724–4732. https://doi.org/10.1109/iccv.2017.505
Li H and Zhang L (2018) Multi-exposure Fusion with CNN Features. in: 2018 25th IEEE International Conference on Image Processing. 1723–1727.https://doi.org/10.1109/ICIP.2018.8451689
Xu H, Ma J, Zhang XP (2020) MEF-GAN: multi-exposure image fusion via generative adversarial networks. IEEE Trans. Image Process. 29:7203–7216. https://doi.org/10.1109/TIP.2020.2999855
Ma K, Duanmu Z, Zhu H, Fang Y, Wang Z (2020) Deep guided learning for fast multi-exposure image fusion. IEEE Trans. Image Process. 29:2808–2819. https://doi.org/10.1109/TIP.2019.2952716
Zhang X (2021) Benchmarking and comparing multi-exposure image fusion algorithms. Information Fusion 74:111–131. https://doi.org/10.1016/j.inffus.2021.02.005
Ma K, Li H, Yong H, Wang Z, Meng D, Zhang L (2017) Robust multi-exposure image fusion: A structural patch decomposition approach. IEEE Trans. Image Process. 26(5):2519–2532. https://doi.org/10.1109/TIP.2017.2671921
Bavirisetti D, Dhuli R (2016) Fusion of infrared and visible sensor images based on anisotropic diffusion and karhunen loeve transform. IEEE Sensors Journal 16(1):203–209. https://doi.org/10.1109/JSEN.2015.2478655
Bavirisetti D, Xiao G and Liu G (2017) Multi-sensor image fusion based on fourth order partial differential equations. 20th International Conference on Information Fusion (Fusion), IEEE, 1–9. https://doi.org/10.23919/ICIF.2017.8009719
Zhou Z, Dong M, Xie X, Gao Z (2016) Fusion of infrared and visible images for night-vision context enhancement. Applied optics 55(23):6480–6490. https://doi.org/10.1364/AO.55.006480
Ma J, Chen C, Li C, andHuang J, (2016) Infrared and visible image fusion via gradient transfer and total variation mini-mization. Information Fusion 31:100–109. https://doi.org/10.1016/j.inffus.2016.02.001
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. https://doi.org/10.1016/j.inffus.2015.11.003
Li H and Wu X (2018) Infrared and visible image fusion using latent low-rank representation. arXiv preprintarXiv:1804.08992. https://doi.org/10.48550/arXiv.1804.08992
Naidu V (2011) Image fusion technique using multi-resolution singular value decomposition. Defence Science Journal 61(5):479–484. https://doi.org/10.14429/dsj.61.705
Bavirisetti DP, Dhuli R (2016) Two-scale image fusion of visible and infrared images using saliency detection. Infrared Physics & Technology 76:52–64. https://doi.org/10.1016/j.infrared.2016.01.009
Li H, Wu X, Durrani TS (2019) Infrared and visible image fusion with resnet and zero-phase component analysis. Infrared Physics & Technology 102:103039. https://doi.org/10.1016/j.infrared.2019.103039
Guo Z, Li X, Huang H, Guo N and Li Q (2018) Medical image segmentation based on multi-modal convolutional neural network: Study on image fusion schemes// Proceedings of 2018 IEEE 15th International Symposium on Biomedical Imaging (ISBI 2018).Washington DC, USA: IEEE:903–907. https://doi.org/10.1109/ISBI.2018.8363717
Wang L, Chang CH, Hao BL and Liu CX (2020) Multi-modal Medical Image Fusion Based on GAN and the Shift-Invariant Shearlet Transform. Proceedings of the 2020 IEEE International Conference on Bioinformatics and Biomedicine (BIBM), Seoul, Korea (South). https://doi.org/10.1109/BIBM49941.2020.9313288
Zhang Y, Zhang L, Bai X, Zhang L (2017) Infrared and visual image fusion through infrared feature extraction and visual information preservation. Infrared Physics & Technology 83:227–237. https://doi.org/10.1016/j.infrared.2017.05.007
Duan J, Chen L and Chen C (2018) Multifocus image fusion with enhanced linear spectral clustering and fast depth map estimation.Neurocomputing, 318:43–54. https://doi.org/10.1016/j.neucom.2018.08.024
Paul S, Sevcenco IS, Agathoklis P (2016) Multi-exposureand multi-focus image fusion in gradient domain. Journal of Circuits, Systems and Computers 25(10):1650123. https://doi.org/10.1142/S0218126616501231
Qiu X, Li M, Zhang L, Yuan X (2019) Guided filter-based multi-focus image fusion through focus region detection. Signal Processing: Image Communication 72:35–46. https://doi.org/10.1016/j.image.2018.12.004
Xu H, Ma J, Le Z, Jiang J and Guo X (2020) FusionDN: A unified densely connected network for image fusion//Proceedings of the AAAI Conference on Artificial Intelligence, 34(07):12484–12491. https://doi.org/10.1609/aaai.v34i07.6936
Li H. Wu X and Kittler J (2018) Infrared and visible image fusion using a deep learning framework. 24th International Conference on Pattern Recognition. https://doi.org/10.1109/ICPR.2018.8546006
Zhang Y, Liu Y, Sun P, Yan H, Zhao X and Zhang L (2020) IFCNN: A general image fusion framework based on convolutional neural network.Information Fusion, 54:99–118. https://doi.org/10.1016/j.inffus.2019.07.011
Liu Y, Liu S, Wang Z (2015) A general framework for image fusion based on multi-scale transform and sparse representation. Information Fusion. 24(1):147–164. https://doi.org/10.1049/iet-ipr.2014.0311
Li S, Kang X, Fang L, Hu v and Yin H, (2017) Pixel-level image fusion: A survey of the state of the art. Information Fusion 33:100–112. https://doi.org/10.1016/j.inffus.2016.05.004
Burt PJ, Adelson EH (1983) The laplacian pyramid as a compact image code. IEEE Trans on Communications 31(4):532–540. https://doi.org/10.1016/B978-0-08-051581-6.50065-9
Li MJ, Dong YB, Wang XL (2014) Image Fusion Algorithm Based on Gradient Pyramid and its Performance Evaluation. Applied Mechanics and Materials 525:715–718. https://doi.org/10.4028/www.scientific.net/AMM.525.715
Toet A (1989) Image fusion by a ratio of low-pass pyramid.Pattern Recognition Letters, 9(4):245–253. https://doi.org/10.1016/0167-8655(89)90003-2
Yan X (2018) Research on Algorithm for Multi-source Image Fusion. XIDIAN University
Hala A, Mohammed E, Eman E, Mohammed E, Ahmed A (2015) Multi-resolution MRI Brain Image Segmentation Based on Morphological Pyramid and Fuzzy C-mean Clustering. Arabian Journal for Science and Engineering 40(11):3173–3185. https://doi.org/10.1007/s13369-015-1791-x
Karakaya D, Ulucan O and Turkan M (2021) PAS-MEF: Multi-exposure image fusion based on principal component analysis, adaptive well-exposedness and saliency map. IEEE:1515–1526. https://doi.org/10.48550/arXiv.2105.11809
Muthiah MA, Logashamugam E and Reddy B (2020) Fusion of MRI and PET Images Using Deep Learning Neural Networks// Proceedings of the 2019 2nd International Conference on Power and Embedded Drive Control (ICPEDC).Chennai, India, IEEE:175–179. https://doi.org/10.1109/ICPEDC47771.2019.9036665
Pajares G and Cruz J (2004) A wavelet-based image fusion tutorial.Pattern Recognition, 37(9):1855–1872. https://doi.org/10.1016/j.patcog.2004.03.010
Li H, Manjunath B and Mitra S (1995) Multisensor image fusion using the wavelet transform.Graphical Models and Image Processing, 57(3):235–245. https://doi.org/10.1006/gmip.1995.1022
Hill P, Canagarajah N and Bull D (2002) Image fusion using complex wavelets// Proceedings of the British Machine Vision Conference 2002 (BMVC).Bristol, UK:487–496. https://doi.org/10.5244/c.16.47
Hammond DK, Vandergheynst P and Gribonval R (2011) Wavelets on graphs via spectral graph theory.Applied and Computational Harmonic Analysis, 30(2): 129–150. https://doi.org/10.1016/j.acha.2010.04.005
Ahmed ST, Sankar S (2020) Investigative protocol design of layer optimized image compression in telemedicine environment. Procedia Computer Science 167(2020):2617–2622. https://doi.org/10.1016/j.procs.2020.03.323
Aymaz S and Kose C (2019) A novel image decomposition-based hybrid technique with super-resolution method for multi-focus image fusion.Information Fusion, 45:113–127. https://doi.org/10.1016/j.inffus.2018.01.015
Liu Y, Liu SP, Wang ZF (2014) Medical Image Fusion by Combining Nonsubsampled Contourlet Transform and Sparse Representation. ChineseConference Pattern Recognition 372–381. https://doi.org/10.1007/978-3-662-45643-9_39
Gao C and Li W (2021) Multi-scale PIIFD for Registration of Multi-source Remote Sensing Images.JOURNAL OF BEIJING INSTITUTE OF TECHNOLOGY, 30(2):113–124. https://doi.org/10.15918/j.jbit1004-0579.2021.016
Ma J, Zhou Z , Bo W and Dong M (2017) Multi-focus image fusion based on multi-scale focus measures and generalized random walk. 2017 36th Chinese Control Conference (CCC). 2017:26–28. https://doi.org/10.23919/ChiCC.2017.8028223
Luo X, Li X , Wang P , Qi S, Guan J and Zhang Z (2018) Infrared and visible image fusion based on NSCT and stacked sparse autoencoders.Multimedia Tools & Applications, 77:22407–22431. https://doi.org/10.1007/s11042-018-5985-6
Dong Z, Lai C, Qi D, Xu Z, Li C and Duan S (2018) A general memristor-based pulse coupled neural network with variable linking coefficient for multi-focus image fusion.Neurocomputing, 308:172–183. https://doi.org/10.1016/j.neucom.2018.04.066
Zhang Y, Wei W, Yuan Y (2019) Multi-focus image fusion with alternating guided filtering. Signal. Image and Video Processing 13(4):727–735. https://doi.org/10.1007/s11760-018-1402-x
Toet A (2016) Alternating guided image filtering.PeerJ Computer Science, 2(e72). https://doi.org/10.7717/peerj-cs.72
Wang Z, Chen L, Li J and Zhu Y (2019) Multi-focus image fusion with random walks and guided filters.Multimedia Systems, 25:323–335. https://doi.org/10.1007/s00530-019-00608-w
Stimpel B, Syben C, Schirrmacher F, Hoelter P, Dórfler A, Maier A (2020) Multi-modal Deep Guided Filtering for Comprehensible Medical Image Processing. IEEE Trans on Medical Imaging 39(5):1703–1711. https://doi.org/10.1109/TMI.2019.2955184
Olshausen BA and Field DJ (1996) Emergence of simple-cell receptive field properties by learning a sparse code for natural images.Nature, 381 (6583):607–609. https://doi.org/10.1038/381607a0
Yang B and Li S (2010) Multifocus image fusion and restoration with sparse representation.IEEE Trans on Instrumentation and Measurement, 59 (4):884–892. https://doi.org/10.1109/TIM.2009.2026612
Yang B and Li S (2012) Pixel-level image fusion with simultaneous orthogonal matching pursuit.Information Fusion, 13 (1):10–19. https://doi.org/10.1016/j.inffus.2010.04.001
Qiu CH, Wang YY, Zhang H, Xia SR (2017) Image fusion of CT and MR with Sparse Representation in NSST Domain. Computational and Mathematical Methods in Medicine 1–13. https://doi.org/10.1155/2017/9308745
Piella G (2009) Image fusion for enhanced visualization: A variational approach. International Journal of Computer Vision 83(1):1–11. https://doi.org/10.1007/s11263-009-0206-4
Manu CS and Jiji CV (2015) A novel remote sensing image fusion algorithm using ICA bases// Proceedings of the 2015 Eighth International Conference on Advances in Pattern Recognition (ICAPR).Kolkata, India, IEEE. https://doi.org/10.1109/icapr.2015.7050690
Zhang CY, Luo XQ, Zhang ZC, Gao Ruichao, Xiaojun Wu (2015) Multi-focus Image Fusion Method Using Higher Order Singular Value Decomposition and Fuzzy Reasoning. Journal of Algorithms & Computational Technology 9(3):303–321. https://doi.org/10.1260/1748-3018.9.3.303
Phamila Y and Amutha R (2014) Discrete cosine transform based fusion of multi-focus images for visual sensor networks.Signal Processing, 95:161–170. https://doi.org/10.1016/j.sigpro.2013.09.001
Zhao C, Wang T and Lei B (2020) Medical image fusion method based on dense block and deep convolutional generative adversarial network. Neural Computing and Applications, (5):1–16. https://doi.org/10.1007/s00521-020-05421-5
Li S, Kwok J and Wang Y (2001) Combination of images with diverse focuses using the spatial frequency.Information Fusion, 2 (3):169–176. https://doi.org/10.1016/S1566-2535(01)00038-0
Chaudhary V and Kumar V (2018) Block-based image fusion using multi-scale analysis to enhance depth of field and dynamic range.Signal Image and Video Processing.12:271–279. https://doi.org/10.1007/s11760-017-1155-y
Li S and Yang B (2008) Multifocus image fusion using region segmentation and spatial frequency.Image and Vision Computing, 26 (7):971–979. https://doi.org/10.1016/j.imavis.2007.10.012
Yang B and Guo L (2015) Superpixel based fusion and demosaicing for multi-focus Bayer images.Optik - International Journal for Light and Electron Optics, 126(23):4460–4468. https://doi.org/10.1016/j.ijleo.2015.08.023
Duan J, Chen L and Chen C (2016) Multifocus image fusion using superpixel segmentation and superpixel-based mean filtering.Applied Optics, 55 (36):10352–10362. https://doi.org/10.1364/AO.55.010352
Qiu X, Li M, Zhang L and Yuan X (2019) Guided filter-based multi- focus image fusion through focus region detection.Signal Processing: Image Communication, 72:35–46. https://doi.org/10.1016/j.image.2018.12.004
Ahmed ST, and Sandhya M (2019) Real-time biomedical recursive images detection algorithm for Indian telemedicine environment. In Cognitive Informatics and Soft Computing: Proceeding of CISC 2017, pp. 723–731. Springer Singapore. https://doi.org/10.1007/978-981-13-0617-4_68
Chai Y, Li H and Li Z (2011) Multifocus image fusion scheme using focused region detection and multiresolution.Optics Communications, 284 (19):4386–4389. https://doi.org/10.1016/j.optcom.2011.05.046
Zhang LX (2020) Research on Pixel-Level Fast Fusion Methods for Multi-Source Images. University of Science and Technology Beijing, 06
Li JX, Guo XB, Lu GM, Zhang B, Xu Y, Wu F, Zhang D (2020) DRPL:deep reression pair learning for multi-focus image fusion. IEEE Transactions on Image Processing 29:4816–4831. https://doi.org/10.1109/TIP.2020.2976190
Jin SP, Yu BB, Jing MH, Zhou Y, Liang JJ, Ji RH (2022) DarkVisionNet:low-light imaging via RGB-NIR fusion with deep inconsistency prior. Proceedings of the AAAI Conference on Artificial Intelligence 36(1):1104–1112. https://doi.org/10.1609/aaai.v36i1.19995
Adeniyi JK, Adeniyi EA, Oguns YJ, Egbedokun GO, Ajagbe KD, Obuzor PC, Ajagbe SA (2022) Comparative Analysis of Machine Learning Techniques for the Prediction of Employee Performance. Paradigmplus 3(3):1–15. https://doi.org/10.55969/paradigmplus.v3n3a1
Ajagbe SA, Oki OA, Oladipupo MA and Nwanakwaugwu A (2022) Investigating the Efficiency of Deep Learning Models in Bioinspired Object Detection. International Conference on Electrical, Computer and Energy Technologies (ICECET). 2022:1–6. https://doi.org/10.1109/ICECET55527.2022.9872568
Adebisi OA, Ajagbe SA, Ojo JA, Oladipupo MA (2022) Computer Techniques for Medical Image Classification: A Review. International Journal of Advanced Computer Research. 03:19–36. https://doi.org/10.1007/978-981-16-8150-9_2
Ajagbe SA, Amuda KA, Oladipupo MA, Afe OF, Okesola KI (2021) Multi-classification of alzheimer disease on magnetic resonance images (MRI) using deep convolutional neural network (DCNN) approaches. International Journal of Advanced Computer Research. 11(53):51–60. https://doi.org/10.19101/IJACR.2021.1152001
Li J, Yuan G and Fan H (2019) Multifocus image fusion using wavelet-domain-based deep CNN.Computational intelligence and neuroscience, 1–23. https://doi.org/10.1155/2019/4179397
Goodfellow IJ, Pouget-Abadie J, Mirza M, Xu B, Warde Farley D, Qzair SJ and Coruville A (2014) Generative adversarial nets. Proceedings of the 27th International Conference on Neural Information Processing Systems.Cambridge: MIT Press, 12(2):2672–2680. https://doi.org/10.5555/2969033.2969125
Guo X, Nie R, Cao J, Zhou D, Mei L, He K, Member S, Nie R, Cao J, Zhou D (2019) FuseGAN: Learning to fuse Multi-focus Image via Conditional Generative Adversarial Network. IEEE Transactions on Multimedia 21(8):1982–1996. https://doi.org/10.1109/TMM.2019.2895292
Li QL, Lu L, Li Z, Wu W, Liu Z, Jeon G, Yang XM (2019) Coupled GAN with Relativistic Discriminators for Infrared and Visible Images Fusion. IEEE Sensors Journal 6:7458–7467. https://doi.org/10.1109/JSEN.2019.2921803
Xu H, Ma J, Jiang J, Guo X, Ling H (2020) U2fusion: A unified unsupervised image fusion network. IEEE Transactions on Pattern Analysis and Machine Intelligence 44(1):502–518. https://doi.org/10.1109/TPAMI.2020.3012548
Qu L, Liu S, Wang M, Song Z (2022) TransMEF: a transformer-based multi-exposure image fusion framework using self-supervised multi-task learning. Proceedings of the AAAI Conference on Artificial Intelligence 36(2):2126–2134. https://doi.org/10.1609/aaai.v36i2.20109
Aardt V and Jan (2008) Assessment of image fusion procedures using entropy, image quality, and multispectral classification. Journal of Applied Remote Sensing, 2(1):1–28. https://doi.org/10.117/1.2945910
Rao YJ (1997) In-fibre bragg grating sensors.Measurement science and technology, 8(4):355–375. https://doi.org/10.1088/0957-0233/8/4/002
Hossny M, Nahavandi S and Creighton D (2008) Comments on information measure for performance of image fusion.Electronics Letters, 44 (18):1066–1067. https://doi.org/10.1049/el:20081754
Wang Q, Shen Y and Zhang J (2005) A nonlinear correlation measure for multivariable data set.Physica D Nonlinear Phenomena 200(3):287–295 . https://doi.org/10.1016/j.physd.2004.11.001
Xydeas CS and Pv V (2000) Objective image fusion performance measure.Military Technical Courier, 36(4):308 – 309. https://doi.org/10.1049/el:20000267
Zhao JY, Laganiere R, Liu Z (2007) Performance assessment of combinative pixel-level image fusion based on an absolute feature measurement. International Journal of Innovative Computing, Information and Control 6(3):1433–1447. https://doi.org/10.1142/S0219622007002654
Piella G and Heijmans H (2003) A new quality metric for image fusion//Proceedings of 10th IEEE International Conferenceon Image Processing (ICIP).Barcelona, Spain, IEEE:173–176. https://doi.org/10.1109/ICIP.2003.1247209
Wang Z and Bovik A (2002) A universal image quality index.IEEE Signal Processing Letters, 9 (3)81–84. https://doi.org/10.1109/97.995823
Zhou W, Bovik AC and Sheikh HR (2004) Image quality assessment: from error visibility to structural similarity.IEEE Transactions on Image Processing, 13 (4):600–612. https://doi.org/10.1109/TIP.2003.819861
Yang C, Zhang J, Wang X and Liu X (2008) A novel similarity based quality metric for image fusion.Information Fusion, 9 (2):156–160 https://doi.org/10.1016/j.inffus.2006.09.001
Chen Y and Blum R (2009) A new automated quality assessment algorithm for image fusion.Image and Vision Computing, 27 (10) :1421–1432. https://doi.org/10.1016/j.imavis.2007.12.002
Chen H and Varshney P (2007) A human perception inspired quality metric for image fusion based on regional information.Information Fusion, 8 (2):193–207. https://doi.org/10.1016/j.inffus.2005.10.001
Liu Z, Blasch E, Xue Z and Zhao J, Laganiere R, Wu W (2012) Objective assessment of multiresolution image fusion algorithmsfor context enhancement in night vision: a comparative study.IEEE Transactions on Pattern Analysis and Machine Intelligence, 34 (1):94–109. https://doi.org/10.1109/TPAMI.2011.109
Bhat S, Koundal D (2021) Multi-focus image fusion techniques: a survey. Artificial Intelligence Review. https://doi.org/10.1007/s10462-021-09961-7
Nejati M, Samavi S, Shirani S (2015) Multi-focus image fusion using dictionary-based sparse representation. Information Fusion 25:72–84. https://doi.org/10.1016/j.inffus.2014.10.004
The Whole Brain Atlas of Harvard Medical School. Accessed: Nov. 2, 2015. Online. Available: http://www.med.harvard.edu/AANLIB/
Zuo Y, Fang Y, Ma K (2023) The critical review of the growth of deep learning-based image fusion techniques. Journal of Image and Graphics 28(01):0102–0117. https://doi.org/10.11834/jig.220556
Author information
Authors and Affiliations
Contributions
Correspondence and requests for materials should be addressed to M.Z.
Corresponding author
Ethics declarations
Conflicts of interest
All authors have participated in (a) conception and design or analysis and interpretation of the data; (b) drafting the article or revising it critically for important intellectual content; and (c) approval of the final version. This manuscript has not been submitted to or is under review at, another journal or other publishing venue. The authors have no affiliation with any organization with a direct or indirect financial interest in the subject matter discussed in the manuscript.
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.
About this article
Cite this article
Li, R., Zhou, M., Zhang, D. et al. A survey of multi-source image fusion. Multimed Tools Appl 83, 18573–18605 (2024). https://doi.org/10.1007/s11042-023-16071-9
Received:
Revised:
Accepted:
Published:
Issue Date:
DOI: https://doi.org/10.1007/s11042-023-16071-9