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.
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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
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DOI: https://doi.org/10.1007/978-981-19-9819-5_18
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