Abstract
Image fusion deals with the ability to integrate data from image sensors at different instants when the source information is uncertain. Although there exist many techniques on the subject, in this paper, we develop two originative techniques based on principal component analysis and slicing image transformation to efficiently fuse a small set of noisy images. For instance, in neural data fusion, this approach requires a considerable number of corrupted images to efficiently produce the desired outcome and also requiring a considerable computing time because of the dynamics involved in the fusion data process. In our approaches, the computation time is considerably smaller. This results appealing to increasing feasibility, for instance, in remote sensing or wireless sensor network. Moreover, and according to our numerical experiments, when our methods are compared against the neural data fusion algorithm, they present better performance.
Highlights
On one hand, data fusion consists in measurements integration from different sensors at different time instants when the original information is uncertain [1,2,3]
By taking into account that image data fusion can be realized by doing a proper linear combination of the acquired image samples at certain instant of times [1, 2, 40,41,42,43], the main objective of this paper is to present two novel numerical techniques on data fusion by using principal component analysis (PCA) and slicing image transformation
In some applications, the set of image to be processed arrives from the same image sensor, which means that these image samples can be considered perfectly aligned
Summary
Data fusion consists in measurements integration from different sensors at different time instants when the original information is uncertain [1,2,3]. Image multi-sensor measurements data fusion refers to the acquisition, processing and synergistic combination of information gathered by various knowledge sources to provide a better understanding of a phenomenon. One containing its filtered version, and in the other an estimation of the noisy entropy affecting the original clean data. To resume this mathematical method, let [X1 X2 · · · XN ] ∈ Rk×N (12).
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