Computer Science

Computer Science ›› 2020, Vol. 47 ›› Issue (11A): 209-214.doi: 10.11896/jsjkx.200100090

• Computer Graphics & Multimedia • Previous Articles     Next Articles

Neural Style Transfer Method Based on Laplace Operator to Suppress Artifacts

ZHANG Mei-yu1, LIU Yue-hui1, QIN Xu-jia1, WU Liang-wu2   

  1. 1 College of Computer Science and Technology,Zhejiang University of Technology,Hangzhou 310023,China
    2 Dalian Institute of Test and Control Technology,Dalian,Liaoning 116013,China
  • Online:2020-11-15 Published:2020-11-17
  • About author:ZHANG Mei-yu,born in 1965,professor.Her research interests include ima-ge analysis and image processing.
    QIN Xu-jia,born in 1968,Ph.D,professor,Ph.D candidate supervisor,is a member of China Computer Federation.His main research interests include computer graphics,image processing and data visualization.
  • Supported by:
    This work was supported the National Natural Science Foundation of China (61672463) and Natural Science Foundation of Zhejiang Pvovince,China (LY20F020025,LY18F020035).

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Abstract: In image neural style transfer technology,most algorithms have artifacts that affect visual effects:checkerboard effects and textures that affect the semantic content of the original image.In this paper,an image style transfer method based on Laplacian suppression artifacts is proposed.Firstly,a transformation network for real-time neural style transfer is redesigned using hole convolution and 1×1 convolution filter kernels.Then,the transformed result is input to VGG for feature map detection,and the multi-layer feature and the original VGG feature are extracted and filtered by Laplace operator to calculate the L1 error.Constrain image changes to suppress artifacts.In the final encoder stage,the image content is modified using an encoder with added dropout.While deepening the network,the model size was controlled by 1×1 convolution filter kernels,which reduced the model size about 6%.Finally,experiments show that the results of this method are better than traditional methods in suppressing artifacts,and can produce images with good visual effects.

Key words: Convolutional Neural Networks, Gram matrix, Laplacian operator, Residual, Style transfer

CLC Number: 

  • TP391
[1] GATYS L A,ECKER A S,BETHGE M.A Neural Algorithm of Artistic Style[J].arXiv:1508.06576,2015.
[2] SIMONYAN K,ZISSERMAN A.Very Deep Convolutional Networks for Large-Scale Image Recognition[C]//International Conference on Learning Representations.2015.
[3] SRIVASTAVA N,HINTON G E,KRIZHEVSKY A,et al.Dropout:a simple way to prevent neural networks from overfitting[J].Journal of Machine Learning Research,2014,15(1):1929-1958.
[4] MISRA D.Mish:A Self Regularized Non-Monotonic Neural Ac-tivation Function[J].arXiv:1908.08681,2019
[5] JOHNSON J,ALAHI A,FEIFEI L.Perceptual Losses for Real-Time Style Transfer and Super-Resolution[C]//European Conference on Computer Vision.2016:694-711.
[6] LONG J,SHELHAMER E,DARRELL T.Fully convolutional networks for semantic segmentation[C]//Computer Vision and Pattern Recognition.2015:3431-3440.
[7] LUAN F,PARIS S,SHECHTMAN E,et al.Deep PainterlyHarmonization[J].Computer Graphics Forum,2018,37(4):95-106.
[8] JING Y,LIU Y,YANG Y,et al.Stroke Controllable Fast Style Transfer with Adaptive Receptive Fields[C]//European Confe-rence on Computer Vision.2018:244-260.
[9] SHEN F,YAN S,ZENG G.Neural Style Transfer via MetaNetworks[C]//Computer Vision and Pattern Recognition.2018:8061-8069.
[10] RUSSAKOVSKY O,DENG J,SU H,et al.ImageNet LargeScale Visual Recognition Challenge[J].International Journal of Computer Vision,2015,115(3):211-252.
[11] HE K,ZHANG X,REN S,et al.Deep Residual Learning forImage Recognition[C]//Computer Vision and Pattern Recognition.2016:770-778.
[12] WANG M,WEI Q.Research on the algorithm of LaplacianSharpening[C]// Proceedings of the 16th Annual Conference on Information Theory of the Chinese Institute of Electronics.2009:464-468.
[13] JIA H,ZHANG Y,LONG G,et al.REsearch on Laplace Image Enhancement Algorithm Optimization Based on OpenCL[J].Chinese Journal of Computers,2012,39(5):271.
[14] YU F,KOLTUN V.Multi-Scale Context Aggregation by DilatedConvolutions[C]//International Conference on Learning Representations.2016.
[15] LIN M,CHEN Q,YAN S.Network In Network[C]//International Conference on Learning Representations.2014.
[16] IOFFE S,SZEGEDY C.Batch Normalization:Accelerating Deep Network Training by Reducing Internal Covariate Shift[C]//International Conference on Machine Learning.2015:448-456.
[17] HINTON G E,SRIVASTAVA N,KRIZHEVSKY A,et al.Improving neural networks by preventing co-adaptation of feature detectors[J].arXiv:1207.0580,2012.
[18] STEINER B,DEVITO Z,CHINTALA S,et al.PyTorch:An Imperative Style,High-Performance Deep Learning Library[C] //Neural Information Processing Systems.2019:8024-8035.
[19] KINGMA D P,BA J.Adam:A Method for Stochastic Optimization[C]//International Conference on Learning Representations.2015.
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