Abstract
Since images leveraged with pertinent information demonstrates a promising involvement in many field of researches such as medical imaging, microscopic imaging, soft computing, smart cities, network transmission, military areas, feature extraction, object identification and tracking, remote sensing, geographical areas, classification, and so on. Whereas, image fusion schemes yields an informative image via integration of two or more than two partly focused images. Universal image fusion technique design is not possible in practical. Each image fusion technique has its own limitations as well as specific application advantages. In this research paper, authors have analyzed five fusion techniques namely guided filtering-based image fusion technique (IFGF), image fusion relied on image matting-based technique (IFIM), image fusion relied on focused region detection (IFFRD), image fusion technique centered on boundary finding (IFBF), and convolution neural network-based fusion technique (CNN). Two performance metrics, i.e., MI and FMI, have been considered to test the performance results. For metric MI, the best performing method is IFBF with a value of 1.144. For FMI metric, the method with best performance is IFBF with a value of 0.666. Lytro multi-focus color image dataset has been used to perform this experimentation work.
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Singh, V., Kaushik, V.D. (2022). A Study of Multi-Focus Image Fusion: State-Of-The-Art Techniques. In: Tiwari, S., Trivedi, M.C., Kolhe, M.L., Mishra, K., Singh, B.K. (eds) Advances in Data and Information Sciences. Lecture Notes in Networks and Systems, vol 318. Springer, Singapore. https://doi.org/10.1007/978-981-16-5689-7_49
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