Wasserstein total variation filtering

TL;DR

This paper introduces a novel Wasserstein-based regularization method for trend filtering in spatiotemporal data, effectively capturing underlying spatial topology and trends in time series videos.

Contribution

It proposes a new Wasserstein total variation filtering approach with an efficient algorithm for spatiotemporal trend estimation, improving over traditional methods.

Findings

Effective preservation of spatiotemporal trends demonstrated in microscopy videos

Outperforms standard trend filtering algorithms in experiments

Algorithm achieves globally optimal solutions efficiently

Abstract

In this paper, we expand upon the theory of trend filtering by introducing the use of the Wasserstein metric as a means to control the amount of spatiotemporal variation in filtered time series data. While trend filtering utilizes regularization to produce signal estimates that are piecewise linear, in the case of $\ell_1$ regularization, or temporally smooth, in the case of $\ell_2$ regularization, it ignores the topology of the spatial distribution of signal. By incorporating the information about the underlying metric space of the pixel layout, the Wasserstein metric is an attractive choice as a regularizer to undercover spatiotemporal trends in time series data. We introduce a globally optimal algorithm for efficiently estimating the filtered signal under a Wasserstein finite differences operator. The efficacy of the proposed algorithm in preserving spatiotemporal trends in time series video is demonstrated in both simulated and fluorescent microscopy videos of the nematode caenorhabditis elegans and compared against standard trend filtering algorithms.