Filter characteristics in image decomposition with singular spectrum analysis

TL;DR

This paper explores how singular spectrum analysis (SSA) can be extended to multidimensional image data, revealing symmetric filter properties and their applications in image denoising.

Contribution

It demonstrates the symmetry properties of filters generated by SSA in multidimensional data and discusses their implications for image processing tasks.

Findings

Eigenvectors are symmetric or antisymmetric, leading to differential filters.

The dominant filter is a smoothing filter for low-frequency components.

Other filters enhance edges or suppress noise, acting as band-pass or high-pass filters.

Abstract

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.