Effective graph sampling of a nonlinear image transform

Abstract

The Discrete Pulse Transform (DPT) makes use of LULU smoothing to decompose a signal into block pulses. The most recent and effective implementation of the DPT is an algorithm called the Roadmaker's Pavage, which uses a graph-based algorithm that produces a hierarchical tree of pulses as its final output. This algorithm has been shown to have important applications in articial intelligence and pattern recognition. Even though the Roadmakerfo's Pavage is an efficient implementation, the theoretical structure of the DPT results in a slow, deterministic algorithm. This paper examines the use of the spectral domain of graphs and designing graph filter banks to downsample the Roadmaker's Pavage algorithm. We investigate the extent to which this speeds up the algorithm and allows parallel processing. Converting graph signals to the spectral domain can also be a costly overhead, and so methods of estimation for filter banks are examined, as well as the design of a good filter bank that may be reused without needing recalculation.