Abstract
Image fusion is an advanced image processing, in which mean-value coordinates (MVC) algorithm based on Poisson image is a fast and effective algorithm. However, the algorithm may have unsatisfactory results if the source image and target image have many variations of color on the image boundary and image details. To solve the problem, this paper proposes two optimization methods, preserving color based on geodesic distance and matching details with modified detail layer. To verify the feasibility of the methods, the improved MVC results are compared with the original MVC results by experiments. The comparison results show that the improved approach can achieve better performance in image fusion.
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Fu, C., Shao, Y., Deng, L., Lu, G. (2014). Improved Mean-Value Coordinates Algorithm for Image Fusion. In: Fei, M., Peng, C., Su, Z., Song, Y., Han, Q. (eds) Computational Intelligence, Networked Systems and Their Applications. ICSEE LSMS 2014 2014. Communications in Computer and Information Science, vol 462. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-662-45261-5_22
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DOI: https://doi.org/10.1007/978-3-662-45261-5_22
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