2014 | OriginalPaper | Buchkapitel
Sparse Regularization of TV-L1 Optical Flow
verfasst von : Joel Gibson, Oge Marques
Erschienen in: Image and Signal Processing
Verlag: Springer International Publishing
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Optical flow is an ill-posed underconstrained inverse problem. Many recent approaches use total variation (TV) to constrain the flow solution to satisfy color constancy. We find that learning a 2D overcomplete dictionary from the total variation result and then enforcing a sparse constraint on the flow improves the result. A new technique using partially-overlapping patches accelerates the calculation. This approach is implemented in a coarse-to-fine strategy. Our results show that combining total variation and a sparse constraint from a learned dictionary is more effective than total variation alone.