沈阳化工大学学报 ›› 2024, Vol. 38 ›› Issue (2): 155-160.doi: 10.3969/j.issn.2095-2198.2024.02.010
基于改进级联细化网络的语义图像合成
1.沈阳化工大学 信息工程学院， 辽宁 沈阳 110142;2.中国科学院 沈阳自动化研究所， 辽宁 沈阳 110016
Semantic Image Synthesis Based on Improved Cascaded Refinement Network
1. Shenyang University of Chemical Technology, Shenyang 110142, China; 2. Shenyang Institute of Automation Chinese Academy of Sciences, Shenyang 110142, China
摘要：
针对级联细化网络（cascaded refinement networks，CRNs）存在合成图像不完整、语义信息丢失、合成的图像颜色差异大的问题，提出一种改进级联细化网络的语义图像合成方法.在级联细化网络中用空间自适应归一化［spatially-adaptive(de)normalization，SPADE］代替层归一化，通过空间自适应的学习调节归一化层中的激活，从而使语义信息更加完整；引入平滑L1损失函数，减少输出图像和对比图像间的颜色差异；引入可学习的空间自适应归一化，增加网络参数的存储容量，能够学习更多的语义信息，使合成的图像质量得到提升.在Cityscapes数据集和GTA5数据集上的试验结果表明：该方法的平均交并比和像素准确性分别比CRNs的提升了31.4%和7.4%，弗雷歇初始距离比CRNs的降低了16.3%.
| ［1］孟智慧.城市公共自行车站点短时出租量预测方法研究［D］.沈阳:沈阳化工大学,2018:35-47. ［2］CHEN T,CHENG M M,TAN P,et al.Sketch2photo:Internet Image Montage［J］ACM Transactions on Graphics,2009,28(5):124. ［3］WANG K F,GOU C,DUAN Y J,et al.Generative Adversarial Networks:Introduction and Outlook［J］.IEEE/CAA Journal of Automatica Sinica,2017,4(4):588-598. ［4］KINGMA D P,WELLING M.Auto-Encoding Variational Bayes［EB/OL］.（2013-12-20）．https://arxiv.org/abs/1312.6114． ［5］GOODFELLOW I J,POUGET-ABADIE J,MIRZA M,et al.Generative Adversarial Nets［C］//Proceeding of the 27th International Conference on Neural Information Processing Systems.Cambridge：MIT Press,2014:2672-2680. ［6］DENTON E,CHINTALA S,SZLAM A,et al.Deep Generative Image Models Using a Laplacian Pyramid of Adversarial Networks［C］//Proceedings of the 28th International Conference on Neural Information Processing Systems.Cambridge：MIT Press,2015:1486-1494. ［7］蔡雨婷,陈昭炯,叶东毅.基于双层级联GAN的草图到真实感图像的异质转换［J］.模式识别与人工智能,2018,31(10):877-886. ［8］RONNEBERGER O,FISCHER P,BROX T.U-Net:Convolutional Networks for Biomedical Image Segmentation［C］//Medical Image Computing and Computer-Assisted Intervention-MICCAI 2015.Cham:Springer,2015:234-241. ［9］XIN D,BISWAL B B.Psychophysiological Interactions in a Visual Checkerboard Task:Reproducibility,Reliability,and the Effects of Deconvolution［J］.Frontiers in Neuroence,2017,11:573. ［10］CHEN Q F,KOLTUN V.Photographic Image Synthesis with Cascaded Refinement Networks［C］//Proceedings of the IEEE New York:2017 IEEE International Conference on Computer Vision(ICCV).Los Alamitos：IEEE Computer Society，2017:1520-1529. ［11］WANG X T,YU K,DONG C,et al.Recovering Realistic Texture in Image Super-Resolution by Deep Spatial Feature Transform［C］//2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition．Los Alamitos：IEEE Computer Society,2018:606-615. ［12］PARK T,LIU M Y,WANG T C,et al.Semantic Image Synthesis with Spatially-Adaptive Normalization［C］//2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition(CVPR).Los Alamitos：IEEE Computer Society,2019:2332-2341. ［13］DOSOVITSKIY A,BROX T.Generating Images with Perceptual Similarity Metrics Based on Deep Networks［C］//Proceedings of the 30th International Conference on Neural Information Processing Systems.Red Hook：Curran Associates Inc.,2016:658-666. ［14］NGUYEN A,CLUNE J,BENGIO Y,et al.Plug & Play Generative Networks:Conditional Iterative Generation of Images in Latent Space［C］//2017 IEEE Conference on Computer Vision and Pattern Recognition(CVPR).Los Alamitos:IEEE Computer Society,2017:3510-3520. ［15］HE K M,ZHANG X Y,REN S Q,et al.Deep Residual Learning for Image Recognition［C］//2016 IEEE Conferenceon Computer Visionand Pattern Recognition(CVPR).Los Alamitos:IEEE Computer Society,2016:770-778. ［16］CORDTS M,OMRAN M,RAMOS S,et al.The Cityscapes Dataset for Semantic Urban Scene Understanding［C］//2016 IEEE Conferenceon Computer Visionand Pattern Recognition(CVPR).Los Alamitos:IEEE Computer Society,2016:3213-3223. ［17］RICHTER S R,VINEET V,ROTH S,et al.laying for Data:Ground Truth from Computer Games［C］//Computer Vision-ECCV 2016.Cham:Springer,2016:102-118. ［18］ISOLA P,ZHU J Y,ZHOU T H,et al.Image-to-Image Translation with Conditional Adversarial Networks［C］//2017 IEEE Conferenceon Computer Visionand Pattern Recognition(CVPR).Los Alamitos:IEEE Computer Society,2017:5967-5976. ［19］ZHAO H S,SHI J P,QI X J,et al.Pyramid Scene Parsing Network［C］//2017 IEEE Conference on Computer Visionand Pattern Recognition(CVPR).Los Alamitos:IEEE Computer Society,2017:6230-6239. |
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