Wasserstein total variation filtering
This work addresses the limitation of ignoring spatial topology in trend filtering for time series analysis, offering a domain-specific improvement for applications like video processing in microscopy.
The paper tackles the problem of trend filtering in time series data by introducing the Wasserstein metric as a regularizer to control spatiotemporal variation, resulting in a globally optimal algorithm that preserves trends in simulated and real video data, outperforming standard methods.
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.