Filter characteristics in image decomposition with singular spectrum analysis
This work provides theoretical insights into filter characteristics for image decomposition, which could aid in image denoising applications, but it appears incremental as it builds on existing singular spectrum analysis methods.
The paper analyzed the symmetry properties of filters generated by singular spectrum analysis when applied to multidimensional data, showing that for 2D images, these filters correspond to differential-type filters with even- or odd-order derivatives, such as smoothing, edge-enhancement, or noise filters.
Singular spectrum analysis is developed as a nonparametric spectral decomposition of a time series. It can be easily extended to the decomposition of multidimensional lattice-like data through the filtering interpretation. In this viewpoint, the singular spectrum analysis can be understood as the adaptive and optimal generation of the filters and their two-step point-symmetric operation to the original data. In this paper, we point out that, when applied to the multidimensional data, the adaptively generated filters exhibit symmetry properties resulting from the bisymmetric nature of the lag-covariance matrices. The eigenvectors of the lag-covariance matrix are either symmetric or antisymmetric, and for the 2D image data, these lead to the differential-type filters with even- or odd-order derivatives. The dominant filter is a smoothing filter, reflecting the dominance of low-frequency components of the photo images. The others are the edge-enhancement or the noise filters corresponding to the band-pass or the high-pass filters. The implication of the decomposition to the image denoising is briefly discussed.