{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,6,4]],"date-time":"2026-06-04T23:26:54Z","timestamp":1780615614999,"version":"3.54.1"},"reference-count":57,"publisher":"Institute of Electrical and Electronics Engineers (IEEE)","license":[{"start":{"date-parts":[[2025,1,1]],"date-time":"2025-01-01T00:00:00Z","timestamp":1735689600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/ieeexplore.ieee.org\/Xplorehelp\/downloads\/license-information\/IEEE.html"},{"start":{"date-parts":[[2025,1,1]],"date-time":"2025-01-01T00:00:00Z","timestamp":1735689600000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-029"},{"start":{"date-parts":[[2025,1,1]],"date-time":"2025-01-01T00:00:00Z","timestamp":1735689600000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-037"}],"funder":[{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["62088102"],"award-info":[{"award-number":["62088102"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["62436005"],"award-info":[{"award-number":["62436005"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["U21A20485"],"award-info":[{"award-number":["U21A20485"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["62311540022"],"award-info":[{"award-number":["62311540022"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["IEEE Trans. on Image Process."],"published-print":{"date-parts":[[2025]]},"DOI":"10.1109\/tip.2025.3526051","type":"journal-article","created":{"date-parts":[[2025,1,10]],"date-time":"2025-01-10T21:03:38Z","timestamp":1736543018000},"page":"552-565","source":"Crossref","is-referenced-by-count":5,"title":["NeuralDiffuser: Neuroscience-Inspired Diffusion Guidance for fMRI Visual Reconstruction"],"prefix":"10.1109","volume":"34","author":[{"ORCID":"https:\/\/orcid.org\/0000-0003-2081-1313","authenticated-orcid":false,"given":"Haoyu","family":"Li","sequence":"first","affiliation":[{"name":"National Key Laboratory of Human-Machine Hybrid Augmented Intelligence, the National Engineering Research Center for Visual Information and Applications, and the Institute of Artificial Intelligence and Robotics, Xi’an Jiaotong University, Xi’an, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Hao","family":"Wu","sequence":"additional","affiliation":[{"name":"School of Electrical Engineering, Xi’an University of Technology, Xi’an, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-1710-3818","authenticated-orcid":false,"given":"Badong","family":"Chen","sequence":"additional","affiliation":[{"name":"National Key Laboratory of Human-Machine Hybrid Augmented Intelligence, the National Engineering Research Center for Visual Information and Applications, and the Institute of Artificial Intelligence and Robotics, Xi’an Jiaotong University, Xi’an, China"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"263","reference":[{"key":"ref1","doi-asserted-by":"publisher","DOI":"10.1109\/TCDS.2020.2987352"},{"key":"ref2","doi-asserted-by":"publisher","DOI":"10.1109\/SMC.2018.00187"},{"key":"ref3","doi-asserted-by":"publisher","DOI":"10.1016\/j.neuroimage.2018.07.043"},{"key":"ref4","doi-asserted-by":"publisher","DOI":"10.3389\/fncom.2019.00021"},{"key":"ref5","doi-asserted-by":"publisher","DOI":"10.23919\/MVA.2019.8757985"},{"key":"ref6","doi-asserted-by":"publisher","DOI":"10.1016\/j.neuroimage.2020.117602"},{"key":"ref7","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR52688.2022.01042"},{"key":"ref8","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR52729.2023.01389"},{"key":"ref9","doi-asserted-by":"publisher","DOI":"10.1038\/s41598-023-42891-8"},{"key":"ref10","doi-asserted-by":"publisher","DOI":"10.1145\/3581783.3613832"},{"key":"ref11","first-page":"24705","article-title":"Reconstructing the mind\u2019s eye: fMRI-to-image with contrastive learning and diffusion priors","volume-title":"Proc. 37th Int. Conf. Neural Inf. Process. Syst.","author":"Scotti"},{"key":"ref12","article-title":"Hierarchical text-conditional image generation with CLIP latents","author":"Ramesh","year":"2022","journal-title":"arXiv:2204.06125"},{"key":"ref13","doi-asserted-by":"publisher","DOI":"10.1038\/s41593-021-00962-x"},{"key":"ref14","article-title":"Brain-optimized inference improves reconstructions of fMRI brain activity","author":"Kneeland","year":"2023","journal-title":"arXiv:2312.07705"},{"key":"ref15","article-title":"Improving visual image reconstruction from human brain activity using latent diffusion models via multiple decoded inputs","author":"Takagi","year":"2023","journal-title":"arXiv:2306.11536"},{"key":"ref16","doi-asserted-by":"publisher","DOI":"10.1109\/WACV57701.2024.00804"},{"key":"ref17","first-page":"1","article-title":"Generative adversarial nets","volume-title":"Proc. Adv. Neural Inf. Process. Syst.","volume":"27","author":"Goodfellow"},{"key":"ref18","doi-asserted-by":"publisher","DOI":"10.48550\/arXiv.1312.6114"},{"key":"ref19","first-page":"6517","article-title":"From voxels to pixels and back: Self-supervision in natural-image reconstruction from fMRI","volume-title":"Proc. 33rd Int. Conf. Neural Inf. Process. Syst.","author":"Beliy"},{"key":"ref20","article-title":"Invertible conditional GANs for image editing","author":"Perarnau","year":"2016","journal-title":"arXiv:1611.06355"},{"key":"ref21","first-page":"12104","article-title":"Training generative adversarial networks with limited data","volume-title":"Proc. Adv. Neural Inf. Process. Syst.","volume":"33","author":"Karras"},{"key":"ref22","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR42600.2020.00813"},{"key":"ref23","first-page":"29624","article-title":"Mind reader: Reconstructing complex images from brain activities","volume-title":"Proc. Adv. Neural Inf. Process. Syst.","author":"Lin"},{"key":"ref24","doi-asserted-by":"publisher","DOI":"10.1109\/IJCNN55064.2022.9892673"},{"key":"ref25","first-page":"107","article-title":"Decoding natural image stimuli from fMRI data with a surface-based convolutional network","volume-title":"Medical Imaging with Deep Learning","author":"Gu","year":"2024"},{"key":"ref26","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR52729.2023.02175"},{"key":"ref27","first-page":"44038","article-title":"MindEye2: Shared-subject models enable fMRI-to-image with 1 hour of data","volume-title":"Proc. 41st Int. Conf. Mach. Learn.","author":"Scotti"},{"key":"ref28","article-title":"See through their minds: Learning transferable neural representation from cross-subject fMRI","author":"Liu","year":"2024","journal-title":"arXiv:2403.06361"},{"key":"ref29","first-page":"8780","article-title":"Diffusion models beat GANs on image synthesis","volume-title":"Proc. 35th Int. Conf. Neural Inf. Process. Syst.","author":"Dhariwal"},{"key":"ref30","article-title":"Classifier-free diffusion guidance","author":"Ho","year":"2022","journal-title":"arXiv:2207.12598"},{"key":"ref31","doi-asserted-by":"publisher","DOI":"10.1109\/ICCV51070.2023.00355"},{"key":"ref32","doi-asserted-by":"publisher","DOI":"10.1609\/aaai.v38i5.28226"},{"key":"ref33","first-page":"11127","article-title":"Uni-ControlNet: All-in-one control to text-to-image diffusion models","volume-title":"Proc. 37th Int. Conf. Neural Inf. Process. Syst.","author":"Zhao"},{"key":"ref34","doi-asserted-by":"publisher","DOI":"10.1109\/ICCV51070.2023.01465"},{"key":"ref35","doi-asserted-by":"publisher","DOI":"10.1145\/3588432.3591560"},{"key":"ref36","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR52729.2023.02154"},{"key":"ref37","doi-asserted-by":"publisher","DOI":"10.1109\/CVPRW59228.2023.00091"},{"key":"ref38","doi-asserted-by":"publisher","DOI":"10.1093\/cercor\/9.1.4"},{"key":"ref39","doi-asserted-by":"publisher","DOI":"10.1016\/j.neuron.2007.05.019"},{"key":"ref40","doi-asserted-by":"publisher","DOI":"10.1167\/jov.23.4.8"},{"key":"ref41","doi-asserted-by":"publisher","DOI":"10.1016\/S0010-9452(08)70539-2"},{"key":"ref42","article-title":"MixCo: Mix-up contrastive learning for visual representation","author":"Kim","year":"2020","journal-title":"arXiv:2010.06300"},{"key":"ref43","article-title":"Score-based generative modeling through stochastic differential equations","author":"Song","year":"2020","journal-title":"arXiv:2011.13456"},{"key":"ref44","doi-asserted-by":"publisher","DOI":"10.1198\/jasa.2011.tm11181"},{"key":"ref45","first-page":"864","article-title":"Noise2score: Tweedie\u2019s approach to self-supervised image denoising without clean images","volume-title":"Proc. Adv. Neural Inf. Process. Syst.","volume":"34","author":"Kim"},{"key":"ref46","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR52733.2024.02173"},{"key":"ref47","doi-asserted-by":"publisher","DOI":"10.1038\/s42256-023-00753-y"},{"key":"ref48","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-319-10602-1_48"},{"key":"ref49","doi-asserted-by":"publisher","DOI":"10.1109\/TIP.2003.819861"},{"key":"ref50","doi-asserted-by":"publisher","DOI":"10.1145\/3065386"},{"key":"ref51","first-page":"8748","article-title":"Learning transferable visual models from natural language supervision","volume-title":"Proc. Int. Conf. Mach. Learn.","volume":"139","author":"Radford"},{"key":"ref52","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2016.308"},{"key":"ref53","first-page":"6105","article-title":"EfficientNet: Rethinking model scaling for convolutional neural networks","volume-title":"Proc. Int. Conf. Mach. Learn.","author":"Tan"},{"key":"ref54","first-page":"9912","article-title":"Unsupervised learning of visual features by contrasting cluster assignments","volume-title":"Proc. Adv. Neural Inf. Process. Syst.","volume":"33","author":"Caron"},{"key":"ref55","doi-asserted-by":"publisher","DOI":"10.1038\/s41467-023-38674-4"},{"key":"ref56","first-page":"2234","article-title":"Improved techniques for training gans","volume-title":"Proc. 30th Int. Conf. Neural Inf. Process. Syst.","author":"Salimans"},{"key":"ref57","volume-title":"Pytorch-Fid: FID Score for PyTorch","author":"Seitzer","year":"2020"}],"container-title":["IEEE Transactions on Image Processing"],"original-title":[],"link":[{"URL":"http:\/\/xplorestaging.ieee.org\/ielx8\/83\/10795784\/10838320.pdf?arnumber=10838320","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,1,20]],"date-time":"2025-01-20T19:10:00Z","timestamp":1737400200000},"score":1,"resource":{"primary":{"URL":"https:\/\/ieeexplore.ieee.org\/document\/10838320\/"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2025]]},"references-count":57,"URL":"https:\/\/doi.org\/10.1109\/tip.2025.3526051","relation":{},"ISSN":["1057-7149","1941-0042"],"issn-type":[{"value":"1057-7149","type":"print"},{"value":"1941-0042","type":"electronic"}],"subject":[],"published":{"date-parts":[[2025]]}}}