{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,2,21]],"date-time":"2025-02-21T10:20:39Z","timestamp":1740133239990,"version":"3.37.3"},"reference-count":106,"publisher":"Institute of Electrical and Electronics Engineers (IEEE)","license":[{"start":{"date-parts":[[2022,1,1]],"date-time":"2022-01-01T00:00:00Z","timestamp":1640995200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/ieeexplore.ieee.org\/Xplorehelp\/downloads\/license-information\/IEEE.html"},{"start":{"date-parts":[[2022,1,1]],"date-time":"2022-01-01T00:00:00Z","timestamp":1640995200000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-029"},{"start":{"date-parts":[[2022,1,1]],"date-time":"2022-01-01T00:00:00Z","timestamp":1640995200000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-037"}],"funder":[{"DOI":"10.13039\/501100012166","name":"National Key Research and Development Program of China","doi-asserted-by":"publisher","award":["2018AAA0100602"],"award-info":[{"award-number":["2018AAA0100602"]}],"id":[{"id":"10.13039\/501100012166","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["IEEE Trans. on Image Process."],"published-print":{"date-parts":[[2022]]},"DOI":"10.1109\/tip.2022.3201710","type":"journal-article","created":{"date-parts":[[2022,9,2]],"date-time":"2022-09-02T19:51:54Z","timestamp":1662148314000},"page":"5841-5855","source":"Crossref","is-referenced-by-count":1,"title":["Arbitrary-Scale Texture Generation From Coarse-Grained Control"],"prefix":"10.1109","volume":"31","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-5315-2740","authenticated-orcid":false,"given":"Yanhai","family":"Gan","sequence":"first","affiliation":[{"name":"School of Computer Science and Technology, Ocean University of China, Qingdao, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-1825-328X","authenticated-orcid":false,"given":"Feng","family":"Gao","sequence":"additional","affiliation":[{"name":"School of Computer Science and Technology, Ocean University of China, Qingdao, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-7012-2087","authenticated-orcid":false,"given":"Junyu","family":"Dong","sequence":"additional","affiliation":[{"name":"School of Computer Science and Technology, Ocean University of China, Qingdao, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-6882-600X","authenticated-orcid":false,"given":"Sheng","family":"Chen","sequence":"additional","affiliation":[{"name":"School of Electronics and Computer Science, University of Southampton, Southampton, U.K."}]}],"member":"263","reference":[{"key":"ref1","doi-asserted-by":"publisher","DOI":"10.1364\/JOSAA.4.002395"},{"key":"ref2","doi-asserted-by":"publisher","DOI":"10.1364\/JOSAA.4.002379"},{"key":"ref3","doi-asserted-by":"publisher","DOI":"10.1109\/10.16456"},{"key":"ref4","doi-asserted-by":"publisher","DOI":"10.1103\/PhysRevLett.73.814"},{"issue":"1","key":"ref5","doi-asserted-by":"crossref","first-page":"49","DOI":"10.1023\/A:1026553619983","article-title":"A parametric texture model based on joint statistics of complex wavelet coefficients","volume":"40","author":"Portilla","year":"2000","journal-title":"Int. J. Comput. Vis."},{"key":"ref6","doi-asserted-by":"publisher","DOI":"10.1109\/ICCV.1999.790383"},{"key":"ref7","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2016.265"},{"key":"ref8","doi-asserted-by":"publisher","DOI":"10.1109\/DCC.2013.30"},{"key":"ref9","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2018.00882"},{"key":"ref10","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2016.272"},{"key":"ref11","first-page":"1349","article-title":"Texture networks: Feed-forward synthesis of textures and stylized images","volume-title":"Proc. ICML","author":"Ulyanov"},{"key":"ref12","article-title":"Texture synthesis with spatial generative adversarial networks","volume-title":"arXiv:1611.08207","author":"Jetchev","year":"2016"},{"key":"ref13","article-title":"Transposer: Universal texture synthesis using feature maps as transposed convolution filter","volume-title":"arXiv:2007.07243","author":"Liu","year":"2020"},{"key":"ref14","first-page":"1","article-title":"A learned representation for artistic style","volume-title":"Proc. ICLR","author":"Dumoulin"},{"key":"ref15","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2017.632"},{"key":"ref16","doi-asserted-by":"publisher","DOI":"10.1109\/TIP.2020.2993407"},{"key":"ref17","doi-asserted-by":"publisher","DOI":"10.1007\/s11263-020-01303-4"},{"key":"ref18","doi-asserted-by":"publisher","DOI":"10.1109\/ICCV.2019.00467"},{"key":"ref19","doi-asserted-by":"publisher","DOI":"10.1109\/TIP.2020.3023773"},{"key":"ref20","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2019.00181"},{"key":"ref21","doi-asserted-by":"publisher","DOI":"10.1109\/TIP.2020.3031184"},{"key":"ref22","doi-asserted-by":"publisher","DOI":"10.1109\/ICME.2017.8019513"},{"key":"ref23","doi-asserted-by":"publisher","DOI":"10.1103\/revmodphys.55.601"},{"key":"ref24","doi-asserted-by":"publisher","DOI":"10.1016\/S0097-8493(00)00113-8"},{"key":"ref25","doi-asserted-by":"publisher","DOI":"10.1016\/j.neucom.2018.02.061"},{"key":"ref26","doi-asserted-by":"publisher","DOI":"10.1145\/218380.218398"},{"key":"ref27","doi-asserted-by":"publisher","DOI":"10.1145\/1276377.1276381"},{"key":"ref28","first-page":"1","article-title":"Auto-encoding variational Bayes","volume-title":"Proc. ICLR","author":"Kingma"},{"key":"ref29","first-page":"2672","article-title":"Generative adversarial nets","volume-title":"Proc. NIPS","author":"Goodfellow"},{"key":"ref30","doi-asserted-by":"publisher","DOI":"10.1145\/1276377.1276382"},{"volume-title":"Markov Fields on Finite Graphs and Lattices","year":"2022","key":"ref31"},{"key":"ref32","first-page":"19","article-title":"Markov random fields in statistics","volume-title":"Disorder in Physical Systems: A Volume in Honour of John M. Hammersley","author":"Clifford","year":"1990"},{"key":"ref33","doi-asserted-by":"publisher","DOI":"10.1016\/0146-664X(80)90019-2"},{"key":"ref34","doi-asserted-by":"publisher","DOI":"10.1109\/TPAMI.1983.4767341"},{"key":"ref35","doi-asserted-by":"publisher","DOI":"10.1109\/TPAMI.1984.4767596"},{"key":"ref36","doi-asserted-by":"publisher","DOI":"10.1109\/TPAMI.1987.4767871"},{"key":"ref37","doi-asserted-by":"publisher","DOI":"10.1109\/34.56204"},{"volume-title":"Markov Random Field Modeling in Image Analysis","year":"2009","author":"Li","key":"ref38"},{"key":"ref39","doi-asserted-by":"publisher","DOI":"10.1109\/TPAMI.2017.2737535"},{"key":"ref40","doi-asserted-by":"publisher","DOI":"10.1111\/1467-9469.00113"},{"key":"ref41","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2016.90"},{"key":"ref42","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2016.308"},{"key":"ref43","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2017.243"},{"key":"ref44","first-page":"4467","article-title":"Dual path networks","volume-title":"Proc. NIPS","author":"Chen"},{"key":"ref45","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2018.00716"},{"key":"ref46","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2018.00745"},{"key":"ref47","article-title":"MobileNets: Efficient convolutional neural networks for mobile vision applications","volume-title":"arXiv:1704.04861","author":"Howard","year":"2017"},{"key":"ref48","first-page":"1747","article-title":"Pixel recurrent neural networks","volume-title":"Proc. ICML","author":"van Oord"},{"key":"ref49","first-page":"1","article-title":"Generating diverse high-fidelity images with VQ-VAE-2","volume-title":"Proc. NeurIPS","author":"Razavi"},{"key":"ref50","doi-asserted-by":"publisher","DOI":"10.23915\/distill.00003"},{"key":"ref51","first-page":"4183","article-title":"High-fidelity image generation with fewer labels","volume-title":"Proc. ICML","author":"Lucic"},{"key":"ref52","first-page":"7354","article-title":"Self-attention generative adversarial networks","volume-title":"Proc. ICML","author":"Zhang"},{"key":"ref53","first-page":"1","article-title":"NICE: Non-linear independent components estimation","volume-title":"Proc. ICLR","author":"Dinh"},{"key":"ref54","first-page":"1278","article-title":"Stochastic backpropagation and approximate inference in deep generative models","volume-title":"Proc. ICML","author":"Rezende"},{"key":"ref55","first-page":"1","article-title":"Density estimation using real NVP","volume-title":"Proc. ICLR","author":"Dinh"},{"key":"ref56","first-page":"10236","article-title":"Glow: Generative flow with invertible 1\u00d71 convolutions","volume-title":"Proc. NeurIPS","author":"Kingma"},{"key":"ref57","article-title":"Conditional generative adversarial nets","volume-title":"arXiv:1411.1784","author":"Mirza","year":"2014"},{"key":"ref58","first-page":"1718","article-title":"Generative moment matching networks","volume-title":"Proc. ICML","author":"Li"},{"key":"ref59","first-page":"258","article-title":"Training generative neural networks via maximum mean discrepancy optimization","volume-title":"Proc. UAI","author":"Dziugaite"},{"key":"ref60","first-page":"1","article-title":"Generative models and model criticism via optimized maximum mean discrepancy","volume-title":"Proc. ICLR","author":"Sutherland"},{"key":"ref61","first-page":"2203","article-title":"MMD GAN: Towards deeper understanding of moment matching network","volume-title":"Proc. NIPS","author":"Li"},{"key":"ref62","first-page":"1","article-title":"Demystifying MMD GANs","volume-title":"Proc. ICLR","author":"Binkowski"},{"key":"ref63","doi-asserted-by":"publisher","DOI":"10.1109\/ICCV.2019.00456"},{"issue":"2","key":"ref64","doi-asserted-by":"crossref","first-page":"125","DOI":"10.1023\/A:1008923215028","article-title":"Annealed importance sampling","volume":"11","author":"Neal","year":"2001","journal-title":"Statist. Comput."},{"key":"ref65","doi-asserted-by":"publisher","DOI":"10.1007\/978-0-387-21736-9"},{"key":"ref66","first-page":"5769","article-title":"Improved training of Wasserstein GANs","volume-title":"Proc. NIPS","author":"Gulrajani"},{"key":"ref67","first-page":"1","article-title":"Large scale GAN training for high fidelity natural image synthesis","volume-title":"Proc. ICLR","author":"Brock"},{"key":"ref68","first-page":"469","article-title":"Learning texture manifolds with the periodic spatial GAN","volume-title":"Proc. ICML","author":"Bergmann"},{"key":"ref69","first-page":"1","article-title":"InGAN: Capturing and retargeting the \u2018DNA\u2019 of a natural image","volume-title":"Proc. ICCV","author":"Shcher"},{"key":"ref70","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-319-46487-9_43"},{"key":"ref71","first-page":"386","article-title":"Universal style transfer via feature transforms","volume-title":"Proc. NISP","author":"Li"},{"key":"ref72","doi-asserted-by":"publisher","DOI":"10.1109\/ICCV.2019.00455"},{"key":"ref73","doi-asserted-by":"publisher","DOI":"10.1117\/12.2302767"},{"key":"ref74","first-page":"1","article-title":"Unsupervised representation learning with deep convolutional generative adversarial networks","volume-title":"Proc. ICLR","author":"Radford"},{"key":"ref75","doi-asserted-by":"publisher","DOI":"10.1109\/ICCV.2017.304"},{"key":"ref76","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-319-46454-1_36"},{"key":"ref77","first-page":"2642","article-title":"Conditional image synthesis with auxiliary classifier GANs","volume-title":"Proc. ICML","author":"Odena"},{"key":"ref78","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2019.00595"},{"key":"ref79","first-page":"1","article-title":"Towards principled methods for training generative adversarial networks","volume-title":"Proc. ICLR","author":"Arjovsky"},{"key":"ref80","article-title":"Wasserstein GAN","volume-title":"arXiv:1701.07875","author":"Arjovsky","year":"2017"},{"key":"ref81","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2015.7298965"},{"key":"ref82","doi-asserted-by":"publisher","DOI":"10.1109\/TIT.1962.1057698"},{"key":"ref83","doi-asserted-by":"publisher","DOI":"10.1109\/TPAMI.2018.2884905"},{"key":"ref84","first-page":"1","article-title":"Spectral normalization for generative adversarial networks","volume-title":"Proc. ICLR","author":"Miyato"},{"key":"ref85","first-page":"2234","article-title":"Improved techniques for training GANs","volume-title":"Proc. NIPS","author":"Salimans"},{"key":"ref86","first-page":"901","article-title":"Weight normalization: A simple reparameterization to accelerate training of deep neural networks","volume-title":"Proc. NIPS","author":"Salimans"},{"key":"ref87","first-page":"3846","article-title":"Is generator conditioning causally related to GAN performance?","volume-title":"Proc. ICML","author":"Odena"},{"key":"ref88","article-title":"Spectral norm regularization for improving the generalizability of deep learning","volume-title":"arXiv:1705.10941","author":"Yoshida","year":"2017"},{"key":"ref89","first-page":"448","article-title":"Batch normalization: Accelerating deep network training by reducing internal covariate shift","volume-title":"Proc. ICML","author":"Ioffe"},{"key":"ref90","first-page":"1","article-title":"SNIPER: Efficient multi-scale training","volume-title":"Proc. NIPS","author":"Singh"},{"key":"ref91","first-page":"1","article-title":"On the quantitative analysis of decoder-based generative models","volume-title":"Proc. ICLR","author":"Wu"},{"key":"ref92","first-page":"1","article-title":"Amortised MAP inference for image super-resolution","volume-title":"Proc. ICLR","author":"S\u00f8nderby"},{"key":"ref93","first-page":"3478","article-title":"Which training methods for GANs do actually converge?","volume-title":"Proc. ICML","author":"Mescheder"},{"key":"ref94","doi-asserted-by":"publisher","DOI":"10.1145\/300776.300778"},{"key":"ref95","doi-asserted-by":"publisher","DOI":"10.1109\/ICPR.2002.1044854"},{"article-title":"The kylberg texture dataset v.1.0","year":"2011","author":"Kylberg","key":"ref96"},{"volume-title":"Vision Texture","year":"2022","key":"ref97"},{"key":"ref98","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2014.461"},{"key":"ref99","first-page":"221","article-title":"Identifying perceptual features of procedural textures","volume":"42","author":"Liu","year":"2013","journal-title":"Perception"},{"key":"ref100","doi-asserted-by":"publisher","DOI":"10.1371\/journal.pone.0130335"},{"key":"ref101","doi-asserted-by":"publisher","DOI":"10.1016\/0042-6989(95)00202-2"},{"key":"ref102","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2019.00453"},{"key":"ref103","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR42600.2020.00813"},{"key":"ref104","first-page":"1","article-title":"A note on the evaluation of generative models","volume-title":"Proc. ICLR","author":"Theis"},{"key":"ref105","first-page":"262","article-title":"Texture synthesis using convolutional neural networks","volume-title":"Proc. NIPS","author":"Gatys"},{"key":"ref106","first-page":"6629","article-title":"GANs trained by a two time-scale update rule converge to a local nash equilibrium","volume-title":"Proc. NIPS","author":"Heusel"}],"container-title":["IEEE Transactions on Image Processing"],"original-title":[],"link":[{"URL":"http:\/\/xplorestaging.ieee.org\/ielx7\/83\/9626658\/09875113.pdf?arnumber=9875113","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2024,3,2]],"date-time":"2024-03-02T06:20:36Z","timestamp":1709360436000},"score":1,"resource":{"primary":{"URL":"https:\/\/ieeexplore.ieee.org\/document\/9875113\/"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2022]]},"references-count":106,"URL":"https:\/\/doi.org\/10.1109\/tip.2022.3201710","relation":{},"ISSN":["1057-7149","1941-0042"],"issn-type":[{"type":"print","value":"1057-7149"},{"type":"electronic","value":"1941-0042"}],"subject":[],"published":{"date-parts":[[2022]]}}}