Mathematics
Optimal Transport Regularization in Inverse Problems
Abstract
This preprint studies how optimal transport penalties can stabilize inverse problems with spatially misaligned observations. The paper proposes a regularization scheme that preserves geometric structure more effectively than standard quadratic penalties in selected imaging settings. Early results are promising, but the full archival PDF is still being prepared for release.
Publication Metadata
Keywords
optimal transportinverse problemsregularizationcomputational analysis
Citation
Lucia Ferrer; Ethan Brooks; Yuri Sokolov (2025). Optimal Transport Regularization in Inverse Problems. Center for Applied Analysis. CAA Preprint 2025-14
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