Thats a unique idea.You should consider the following idea to enhance your idea so that it would impact uniquely: Empirical mode decomposition (EMD) is a fully data driven technique for decomposing signals into their natural scale components. Hover the problem of uniqueness, caused by the empirical nature of the algorithm and its sensitivity to changes in parameters, makes it difficult to perform fusion of data from multiple and heterogeneous sources. A solution to this problem is proposed using recent complex extensions of EMD which guarantees the same number of decomposition levels, that is the uniqueness of the scales. The methodology is used to address multifocus image fusion, whereby two or more partially defocused images are combined in automatic fashion so as to create an all in focus image.
['David Looney', 'Danilo P. Mandic']
I am writing a research paper on a idea Multiscale Image Fusion Using Complex Extensions of EMD
[{"from": "human", "value": "I am writing a research paper on a idea Multiscale Image Fusion Using Complex Extensions of EMD"}, {"from": "gpt", "value": "Thats a unique idea.You should consider the following idea to enhance your idea so that it would impact uniquely: Empirical mode decomposition (EMD) is a fully data driven technique for decomposing signals into their natural scale components. Hover the problem of uniqueness, caused by the empirical nature of the algorithm and its sensitivity to changes in parameters, makes it difficult to perform fusion of data from multiple and heterogeneous sources. A solution to this problem is proposed using recent complex extensions of EMD which guarantees the same number of decomposition levels, that is the uniqueness of the scales. The methodology is used to address multifocus image fusion, whereby two or more partially defocused images are combined in automatic fashion so as to create an all in focus image."}]