A novel generative adversarial net for calligraphic tablet images denoising

Chinese calligraphic images have important historical and artistic value, but natural weathering and man-made decay severely damage these works, thus image denoising is an important topic to be addressed. Traditional denoising methods still leave room for improvement. In this paper, image denoising is modeled as generation of clean image by using GAN (Goodfellow I et al. Advances in Neural Information Processing Systems 2672–2680, 2014 ) with an embedment of residual dense blocks (Zhang Y et al. The IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2018 ) that was formerly used for super resolution reconstruction. Meanwhile, a new type of noise is defined to simulate the real noise, and is used for compensation of unpaired data in the training set for GAN. The new structure, used with some preprocessing and training methods, yield satisfactory results compared to known denoising methods.

[1]  Lei Zhang,et al.  Beyond a Gaussian Denoiser: Residual Learning of Deep CNN for Image Denoising , 2016, IEEE Transactions on Image Processing.

[2]  Alexei A. Efros,et al.  Image-to-Image Translation with Conditional Adversarial Networks , 2016, 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR).

[3]  Yuan Yu,et al.  TensorFlow: A system for large-scale machine learning , 2016, OSDI.

[4]  Kilian Q. Weinberger,et al.  Densely Connected Convolutional Networks , 2016, 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR).

[5]  Xingjun Zhang,et al.  Sketch-Based Image Retrieval With Multi-Clustering Re-Ranking , 2020, IEEE Transactions on Circuits and Systems for Video Technology.

[6]  Alessandro Foi,et al.  Image Denoising by Sparse 3-D Transform-Domain Collaborative Filtering , 2007, IEEE Transactions on Image Processing.

[7]  Leon A. Gatys,et al.  Image Style Transfer Using Convolutional Neural Networks , 2016, 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR).

[8]  Jimmy Ba,et al.  Adam: A Method for Stochastic Optimization , 2014, ICLR.

[9]  Rynson W. H. Lau,et al.  FormResNet: Formatted Residual Learning for Image Restoration , 2017, 2017 IEEE Conference on Computer Vision and Pattern Recognition Workshops (CVPRW).

[10]  拓海 杉山,et al.  “Unpaired Image-to-Image Translation using Cycle-Consistent Adversarial Networks”の学習報告 , 2017 .

[11]  Andrew Zisserman,et al.  Incremental learning of object detectors using a visual shape alphabet , 2006, 2006 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'06).

[12]  Andrea Vedaldi,et al.  Instance Normalization: The Missing Ingredient for Fast Stylization , 2016, ArXiv.

[13]  Geoffrey E. Hinton,et al.  ImageNet classification with deep convolutional neural networks , 2012, Commun. ACM.

[14]  Aaron C. Courville,et al.  Improved Training of Wasserstein GANs , 2017, NIPS.

[15]  Nguyen Linh-Trung,et al.  The Laplacian pyramid with rational scaling factors and application on image denoising , 2010, 10th International Conference on Information Science, Signal Processing and their Applications (ISSPA 2010).

[16]  Chuan Li,et al.  Precomputed Real-Time Texture Synthesis with Markovian Generative Adversarial Networks , 2016, ECCV.

[17]  Thomas Brox,et al.  U-Net: Convolutional Networks for Biomedical Image Segmentation , 2015, MICCAI.

[18]  Minghua Zhao,et al.  An integrated method for ancient Chinese tablet images de-noising based on assemble of multiple image smoothing filters , 2016, Multimedia Tools and Applications.

[19]  Léon Bottou,et al.  Wasserstein Generative Adversarial Networks , 2017, ICML.

[20]  Soumith Chintala,et al.  Unsupervised Representation Learning with Deep Convolutional Generative Adversarial Networks , 2015, ICLR.

[21]  Yoshua Bengio,et al.  Generative Adversarial Nets , 2014, NIPS.

[22]  Cem Kalyoncu,et al.  A weighted mean filter with spatial-bias elimination for impulse noise removal , 2015, Digit. Signal Process..

[23]  Lianwen Jin,et al.  High performance offline handwritten Chinese character recognition using GoogLeNet and directional feature maps , 2015, 2015 13th International Conference on Document Analysis and Recognition (ICDAR).

[24]  Ningning Zhou,et al.  An improved filtering algorithm based on median filtering algorithm and medium filtering algorithm , 2012, 2012 IEEE Fifth International Conference on Advanced Computational Intelligence (ICACI).

[25]  Raymond Y. K. Lau,et al.  Least Squares Generative Adversarial Networks , 2016, 2017 IEEE International Conference on Computer Vision (ICCV).

[26]  Fei Yin,et al.  Online and offline handwritten Chinese character recognition: Benchmarking on new databases , 2013, Pattern Recognit..

[27]  Eero P. Simoncelli,et al.  Image quality assessment: from error visibility to structural similarity , 2004, IEEE Transactions on Image Processing.

[28]  Simon Osindero,et al.  Conditional Generative Adversarial Nets , 2014, ArXiv.

[29]  Lei Zhu,et al.  Unsupervised Topic Hypergraph Hashing for Efficient Mobile Image Retrieval , 2017, IEEE Transactions on Cybernetics.

[30]  Masaki Nakagawa,et al.  'Online recognition of Chinese characters: the state-of-the-art , 2004, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[31]  Minghua Zhao,et al.  A Chinese character structure preserved denoising method for Chinese tablet calligraphy document images based on KSVD dictionary learning , 2017, Multimedia Tools and Applications.

[32]  Trevor Darrell,et al.  Rich Feature Hierarchies for Accurate Object Detection and Semantic Segmentation , 2013, 2014 IEEE Conference on Computer Vision and Pattern Recognition.

[33]  Andrew L. Maas.  Rectifier Nonlinearities Improve Neural Network Acoustic Models , 2013 .

[34]  Khouloud Guemri,et al.  Adaptative shock filter for image characters enhancement and denoising , 2014, 2014 6th International Conference of Soft Computing and Pattern Recognition (SoCPaR).

[35]  Xiaochun Cao,et al.  Enhancing Sketch-Based Image Retrieval by CNN Semantic Re-ranking , 2020, IEEE Transactions on Cybernetics.

[36]  Yun Fu,et al.  Residual Dense Network for Image Super-Resolution , 2018, 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition.

[37]  Hsi-Jian Lee,et al.  Dual-binarization and anisotropic diffusion of Chinese characters in calligraphy documents , 2001, Proceedings of Sixth International Conference on Document Analysis and Recognition.