Abstract
Digital image fusion is an important utility for integrating individual information from different modularities. The application domain of digital image fusion is depending on the kind of images fused. These days, the application of infrared and visible images is increased owing to the suitability of these images in industrial, military, local surveillance systems, etc. Based on the variation in radiation, infrared (IR) images can differentiate targets from their backgrounds; on the other hand, visible (VI) images can convey textural details with great spatial details to the human visual system. So, fusing IR-VI image pairs is useful because it integrates the benefits of heat radiation with rich texture data. On the other hand, owing to the growth of modern image acquisition systems, transfer domain techniques, and representation techniques, various image fusion methods are developed in recent years. Among them, sparse representation (SR) methods are one method for signal representation, as well as it is also utilized as the key representation technique in the case of digital image fusion. This paper proposes a technique for IR-VI image fusion by using a modified sparse-based image fusion framework by the BLOTLESS-update dictionary algorithm. The modified framework is tested for IR-VI image fusion on fifty image pairs of the TNO dataset. As for fair comparison, the results are compared with the fusion of the same dataset using learned dictionaries by MOD, KSVD, and SIMCO algorithms. As far as the results are compared the fusion performed by the modified fusion framework using the BLOTLESS-update dictionary learning method is more promising than other benchmark dictionary learning algorithms in most cases.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
References
Kaur, H., Koundal, D., Kadyan, V.: Image fusion techniques: a survey. Arch. Comput. Methods Eng. 28(7), 4425–4447. https://doi.org/10.1007/s11831-021-09540-7
Stéphane, M.: Sparse representations. A wavelet tour of signal processing. Elsevier, pp. 1–31 (2009). https://doi.org/10.1016/B978-0-12-374370-1.00005-7
Yang, B., Li, S.: Multifocus image fusion and restoration with sparse representation. IEEE Trans. Instrum. Meas. 59(4), pp. 884–892 (2010). https://doi.org/10.1109/TIM.2009.2026612
Wang, J., et al.: Fusion method for IR and VI Images by using non-negative sparse representation. IR Phys. Technol. 67, 477–489 (2014). https://doi.org/10.1016/j.IR.2014.09.019
Liu, Y., Wang, Z.: Simultaneous Image fusion and denoising with adaptive sparse representation. IET Image Process. 9(5), 347–357 (2015). https://doi.org/10.1049/iet-ipr.2014.0311
Lu, X., et al.: The IR and VI image fusion algorithm based on target separation and sparse representation. IR Phys. Technol. 67, 397–407 (2014). https://doi.org/10.1016/j.IR.2014.09.007
Liu, Z., et al.: A novel fusion scheme for VI and IR images based on compressive sensing. Opt. Commun. 335, 168–177 (2015). https://doi.org/10.1016/j.optcom.2014.07.093
Liu, Y., et al.: Image fusion with convolutional sparse representation. IEEE Sig. Process. Lett. 23(12), 1882–1886 (2016). https://doi.org/10.1109/LSP.2016.2618776
Yin, L., et al.: A novel image fusion framework based on sparse representation and pulse coupled neural network. IEEE Access 7, 98290–98305 (2019). https://doi.org/10.1109/ACCESS.2019.2929303
Liu, Y., et al.: IR and VI image fusion through details preservation. Sensors 19(20), 4556 (2019). https://doi.org/10.3390/s19204556
Xing, X., et al.: IR and VI Image fusion based on nonlinear enhancement and NSST decomposition. EURASIP J. Wirel. Commun. Netw. 2020(1), 162 (2020). https://doi.org/10.1186/s13638-020-01774-6
Shao, L., et al.: IR and VI image fusion based on spatial convolution sparse representation. J. Phys.: Conf. Ser. 1634(1), 012113 (2020). https://doi.org/10.1088/1742-6596/1634/1/012113
Su Dutta, S., Banerjee, A.: Highly precise modified blue whale method framed by blending bat and local search algorithm for the optimality of image fusion algorithm. J. Soft Comput. Paradigm (JSCP) 2(04), 195–208 (2020). https://doi.org/10.36548/jscp.2020.4.001
Xu, Y., et al.: A survey of dictionary learning algorithms for face recognition. IEEE Access 5, 8502–8514 (2017). https://doi.org/10.1109/ACCESS.2017.2695239
Aharon, M., et al.: $rm K$-SVD: an algorithm for designing overcomplete dictionaries for sparse representation. IEEE Trans. Sig. Process. 54(11), 4311–4322 (2006). https://doi.org/10.1109/TSP.2006.881199
Dai, W., et al.: Simultaneous codeword optimization (SimCO) for dictionary update and learning (2011). https://doi.org/10.48550/ARXIV.1109.5302
Yu, Q., et al.: Dictionary learning with BLOTLESS update. IEEE Trans. Sig. Process. 68, 1635–1645 (2020). https://doi.org/10.1109/TSP.2020.2971948
Saini, L.K., Mathur, P.: Medical image fusion by sparse-based modified fusion framework using block total least-square update dictionary learning algorithm. J. Med. Imaging 9(5), 052403. https://doi.org/10.1117/1.JMI.9.5.052403
Cai, T.T., Wang, L.: Orthogonal matching pursuit for sparse signal recovery with noise. IEEE Trans. Inf. Theory 57(7), 4680–4688 (2011). https://doi.org/10.1109/TIT.2011.2146090
Toet, A.: The TNO multiband image collection (2017). https://doi.org/10.6084/M9.FIGSHARE.C.3860689.V1
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2023 The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.
About this paper
Cite this paper
Saini, L.K., Mathur, P. (2023). IR-VI Image Fusion by SR-Based Modified Fusion Framework. In: Smys, S., Tavares, J.M.R.S., Shi, F. (eds) Computational Vision and Bio-Inspired Computing. Advances in Intelligent Systems and Computing, vol 1439. Springer, Singapore. https://doi.org/10.1007/978-981-19-9819-5_18
Download citation
DOI: https://doi.org/10.1007/978-981-19-9819-5_18
Published:
Publisher Name: Springer, Singapore
Print ISBN: 978-981-19-9818-8
Online ISBN: 978-981-19-9819-5
eBook Packages: Intelligent Technologies and RoboticsIntelligent Technologies and Robotics (R0)