Abstract
An image is generally formed as the composition of salient structures and complex textures. While structures are important for human perception and image analysis, structure extraction from textures remains a challenging issue to be investigated. Even though several methods have been proposed to do this job, they commonly have to balance between texture removing and structure preservation. One problem is that few methods take structural contours into consideration. In this paper, we propose a new learning-based weighted total variation (LTV)model, where the weights are learned from different kinds of texture images to well discriminate pixels belonging to structural contours from pixels belonging to textures. The Chambolles projection method is utilized to solve the optimization problem. Experimental results show that compared with the competing methods, the proposed algorithm performs better in preserving sharp structures while removing textures.
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Acknowledgement
This work is partly support by the National Science Foundation of China (NSFC) project under the contract No. 61271093.
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Zheng, S., Song, C., Zhang, H., Yan, Z., Zuo, W. (2016). Learning-Based Weighted Total Variation for Structure Preserving Texture Removal. In: Tan, T., Li, X., Chen, X., Zhou, J., Yang, J., Cheng, H. (eds) Pattern Recognition. CCPR 2016. Communications in Computer and Information Science, vol 663. Springer, Singapore. https://doi.org/10.1007/978-981-10-3005-5_13
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