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Fabric Texture Removal with Deep Convolutional Neural Networks

  • Li Hou
  • Xiaodong Yue
  • Xiao Xiao
  • Wei Xu
Conference paper
Part of the Advances in Intelligent Systems and Computing book series (AISC, volume 849)

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.

Keywords

Fabric texture removal Deep convolutional neural network 

Notes

Acknowledgements

This work reported here was financially supported by the National Natural Science Foundation of China (Grant No. 61573235).

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Copyright information

© Springer Nature Switzerland AG 2019

Authors and Affiliations

  1. 1.Shanghai Institute for Advanced Communication and Data ScienceShanghai UniversityShanghaiChina
  2. 2.School of Computer Engineering and ScienceShanghai UniversityShanghaiChina

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