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Communication Dans Un Congrès Année : 2022

A Patch-Based Approach for Artistic Style Transfer via Constrained Multi-Scale Image Matching

Résumé

Since a few years and the advent of convolutional neural networks, algorithms for artistic style transfer between images have developed considerably. However, these methods require a relatively long training phase in order to succeed. This is why non-learning image processing approaches recently strove to propose patch-based algorithms able to aesthetically compete with neural methods. This paper goes one step further in this direction by introducing a new patch-based method for style transfer, using a constrained multi-scale version of the fast approximate nearest-neighbor algorithm PatchMatch, enforcing uniform sampling of style featurepatch. Our method also aims to mix the patch-based and neural paradigms by enabling the embedding of image patches in the feature space of the VGG-16 network.
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hal-03826671 , version 1 (24-10-2022)

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Benjamin Samuth, David Tschumperlé, Julien Rabin. A Patch-Based Approach for Artistic Style Transfer via Constrained Multi-Scale Image Matching. IEEE International Conference on Image Processing (ICIP'2022), Oct 2022, Bordeaux, France. ⟨10.1109/ICIP46576.2022.9897334⟩. ⟨hal-03826671⟩
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