Fabric Texture Removal with Deep Convolutional Neural Networks
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.
KeywordsFabric texture removal Deep convolutional neural network
This work reported here was financially supported by the National Natural Science Foundation of China (Grant No. 61573235).
- 5.Fu, X., Huang, J., Zeng, D., Huang, Y., Ding, X., Paisley, J. Removing rain from single images via a deep detail network. In: CVPR, pp. 1715–1723 (2017)Google Scholar
- 6.He, K., Zhang, X., Ren, S., Sun, J. Deep residual learning for image recognition. In: CVPR, pp. 770–778 (2015)Google Scholar
- 7.Simonyan, K., Zisserman, A. Very deep convolutional networks for large-scale image recognition. In: Computer Science (2014)Google Scholar