Abstract
In this paper, we propose a neural network based on Deep Convolutional Neural Network (DCNN) to remove fabric textures from scanned images. Different from the traditional DCNN performed on original images, the proposed network focuses on extracting texture structures and utilizes the texture layers of fabric images for model training. To achieve the precise extraction of fabric textures, the proposed model adopts a network architecture which is inspired by the deep residual network (ResNet). Experiment results on multiple kinds of fabric images validate that the proposed network is effective to remove fabric textures and achieves better performances than other kinds of denoising methods.
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Acknowledgements
This work reported here was financially supported by the National Natural Science Foundation of China (Grant No. 61573235).
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Hou, L., Yue, X., Xiao, X., Xu, W. (2019). Fabric Texture Removal with Deep Convolutional Neural Networks. In: Wong, W. (eds) Artificial Intelligence on Fashion and Textiles. AITA 2018. Advances in Intelligent Systems and Computing, vol 849. Springer, Cham. https://doi.org/10.1007/978-3-319-99695-0_34
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DOI: https://doi.org/10.1007/978-3-319-99695-0_34
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