Abstract
Images generally contain rich visual information that can be decomposed into edges and textures. Particularly, human beings are more sensitive to edge information. However, it is difficult to separate edges from textures since they are tough to be differentiated by computers. In this paper, we provide a novel learning-based bilateral filter to effectively remove textures from the image. Firstly, edge features are extracted as the guidance image through structured forests learning method. Then the guidance image with very rough edge features needs to be optimized. Finally, the joint bilateral filter is applied to produce the filtered result according to the input image and the optimized guidance image. Comparing with some previous approaches, our method is simpler and faster, as well as more effective in preserving edge structures and removing textures.
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Acknowledgements
We would like to thank the anonymous reviewers for their valuable comments. We would also like to thank the People Programme (Marie Curie Actions) of the European Union’s Seventh Framework Programme FP7/2007-2013/ under REA grant agreement n\(^{\circ }\) [612627] for their support.
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Zhang, Z., Xu, P. (2017). An Efficient Learning-Based Bilateral Texture Filter for Structure Preserving. In: Chang, J., Zhang, J., Magnenat Thalmann, N., Hu, SM., Tong, R., Wang, W. (eds) Next Generation Computer Animation Techniques. AniNex 2017. Lecture Notes in Computer Science(), vol 10582. Springer, Cham. https://doi.org/10.1007/978-3-319-69487-0_11
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DOI: https://doi.org/10.1007/978-3-319-69487-0_11
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