Graph Spectral Image Processing. Gene Cheung

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1 Here, we assume both x and y are finite length signals and their boundaries are extended or filtered by a boundary filter to ensure that the equation is valid.

2 While the computation cost for eigendecomposition of a sparse matrix is generally lower than , it still requires a high computational complexity, especially for large graphs.

3 The term “graph signal” was first introduced in Taubin et al. (1996), to the best of our knowledge.

4 In fact, this R can also be used for the reconstruction of the undersampled systems.

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