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• W2044722017 abstract "Image fusion refers to the techniques that integrate complementary information from multiple image sensors’ data in a way that makes the new images more suitable for human visual perception and reduces computation processing tasks. In this paper, we propose a non-parametric and region-based image fusion based on the Bootstrap sampling (BS) principle, which reduces the dependence effect of pixels in real images and minimizes the fusion time. Given an original image, we randomly select a small representative set of pixels. In the statistical image formation model, image sensors are described as the true scene corrupted by additive non-Gaussian distortion. Then, a Non-parametric Expectation–Maximization (NEM) algorithm would be used to estimate both the model parameters and the fused image. The non-parametric aspect comes from the use of the orthogonal series’ estimator. Obtained results show that the BS method gives better results than the classical one, both for fused image quality as well as for computation time." @default.
• W2044722017 created "2016-06-24" @default.
• W2044722017 creator A5001889109 @default.
• W2044722017 date "2010-04-01" @default.
• W2044722017 modified "2023-10-16" @default.
• W2044722017 title "Non-parametric and region-based image fusion with Bootstrap sampling" @default.
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• W2044722017 doi "https://doi.org/10.1016/j.inffus.2008.08.004" @default.
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