Feb. 13, 2024, 5:48 a.m. | Liping Yin Albert Chua

cs.CV updates on arXiv.org arxiv.org

In the past decade, exemplar-based texture synthesis algorithms have seen strong gains in performance by matching statistics of deep convolutional neural networks. However, these algorithms require regularization terms or user-added spatial tags to capture long range constraints in images. Having access to a user-added spatial tag for all situations is not always feasible, and regularization terms can be difficult to tune. Thus, we propose a new set of statistics for texture synthesis based on Sliced Wasserstein Loss, create a multi-scale …

algorithms constraints convolutional neural networks cs.cv images loss networks neural networks performance regularization spatial statistics synthesis tag tags terms texture

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