Paper Info
Title
Fast optimal transport regularized projection and application to coefficient shrinkage and filtering
Abstract
This paper explores solutions to the problem of regularized projections with respect to the optimal transport metric. Expanding recent works on optimal transport dictionary learning and non-negative matrix factorization, we derive general purpose algorithms for projecting on any set of vectors with any regularization, and we further propose fast algorithms for the special cases of projecting onto invertible or orthonormal bases. Noting that pass filters and coefficient shrinkage can be seen as regularized projections under the Euclidean metric, we show how to use our algorithms to perform optimal transport pass filters and coefficient shrinkage. We give experimental evidence that using the optimal transport distance instead of the Euclidean distance for filtering and coefficient shrinkage leads to reduced artifacts and improved denoising results.
Year
DOI
Venue
2022
10.1007/s00371-020-02029-7
The Visual Computer
Keywords
DocType
Volume
Optimal transport, Wasserstein distance, Coefficient shrinkage, Sparse decomposition, Wavelet thresholding, Denoising
Journal
38
Issue
ISSN
Citations 
2
0178-2789
0
PageRank 
References 
Authors
0.34
0
2
Name
Order
Citations
PageRank
Antoine Rolet1272.54
Vivien Seguy2152.78