Optimal Image Transport on Sparse Dictionaries
Junqing Huang Haihui Wang Andreas Weiermann Michael Ruzhansky
In this paper, we derive a novel optimal image transport algorithm over
sparse dictionaries by taking advantage of Sparse Representation (SR) and
Optimal Transport (OT). Concisely, we design a unified optimization framework
in which the individual image features (color, textures, styles, etc.) are
encoded using sparse representation compactly, and an optimal transport plan is
then inferred between two learned dictionaries in accordance with the encoding
process. This paradigm gives rise to a simple but effective way for
simultaneous image representation and transformation, which is also empirically
solvable because of the moderate size of sparse coding and optimal transport
sub-problems. We demonstrate its versatility and many benefits to different
image-to-image translation tasks, in particular image color transform and
artistic style transfer, and show the plausible results for photo-realistic
transferred effects.