76‐3:用于高保真计算机生成全息的改进无监督视觉变压器网络

Chao Xu, Zhenxing Dong, Shuyi Chen, Yan Li, Yuye Ling, Yikai Su
{"title":"76‐3:用于高保真计算机生成全息的改进无监督视觉变压器网络","authors":"Chao Xu, Zhenxing Dong, Shuyi Chen, Yan Li, Yuye Ling, Yikai Su","doi":"10.1002/sdtp.16758","DOIUrl":null,"url":null,"abstract":"To generate high quality images with faster calculation speed for holographic displays, we propose a modified unsupervised Vision Transformer model, which has the capability of capturing global features of an image. The proposed method can infer a hologram significantly faster than the stochastic gradient descent method, while producing images with similar quality.","PeriodicalId":21706,"journal":{"name":"SID Symposium Digest of Technical Papers","volume":null,"pages":null},"PeriodicalIF":0.0000,"publicationDate":"2023-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"76‐3: A Modified Unsupervised Vision Transformer Network for High‐fidelity Computer‐generated Holography\",\"authors\":\"Chao Xu, Zhenxing Dong, Shuyi Chen, Yan Li, Yuye Ling, Yikai Su\",\"doi\":\"10.1002/sdtp.16758\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"To generate high quality images with faster calculation speed for holographic displays, we propose a modified unsupervised Vision Transformer model, which has the capability of capturing global features of an image. The proposed method can infer a hologram significantly faster than the stochastic gradient descent method, while producing images with similar quality.\",\"PeriodicalId\":21706,\"journal\":{\"name\":\"SID Symposium Digest of Technical Papers\",\"volume\":null,\"pages\":null},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2023-06-01\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"SID Symposium Digest of Technical Papers\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1002/sdtp.16758\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"SID Symposium Digest of Technical Papers","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1002/sdtp.16758","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 0

摘要

为了提高全息显示的图像质量和计算速度,提出了一种改进的无监督视觉变换模型,该模型具有捕获图像全局特征的能力。该方法可以比随机梯度下降法更快地推断出全息图,同时产生的图像质量相似。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
查看原文
分享 分享
微信好友 朋友圈 QQ好友 复制链接
本刊更多论文
76‐3: A Modified Unsupervised Vision Transformer Network for High‐fidelity Computer‐generated Holography
To generate high quality images with faster calculation speed for holographic displays, we propose a modified unsupervised Vision Transformer model, which has the capability of capturing global features of an image. The proposed method can infer a hologram significantly faster than the stochastic gradient descent method, while producing images with similar quality.
求助全文
通过发布文献求助,成功后即可免费获取论文全文。 去求助
来源期刊
自引率
0.00%
发文量
0
期刊最新文献
P‐64: Investigation of Fabrication Technologies of GaN‐based Micro‐LED Devices P‐67: Research of photoelectric performance GaN based green Micro‐LED 77‐2: Quantum‐Dot Color Conversion Achieved by A Novel Structure of Hollow Cylindrical Blue MicroLED P‐68: High Luminance Blue Micro‐LEDs in 4×4 and 8×8 Array 76‐3: A Modified Unsupervised Vision Transformer Network for High‐fidelity Computer‐generated Holography
×
引用
GB/T 7714-2015
复制
MLA
复制
APA
复制
导出至
BibTeX EndNote RefMan NoteFirst NoteExpress
×
×
提示
您的信息不完整,为了账户安全,请先补充。
现在去补充
×
提示
您因"违规操作"
具体请查看互助需知
我知道了
×
提示
现在去查看 取消
×
提示
确定
0
微信
客服QQ
Book学术公众号 扫码关注我们
反馈
×
意见反馈
请填写您的意见或建议
请填写您的手机或邮箱
已复制链接
已复制链接
快去分享给好友吧!
我知道了
×
扫码分享
扫码分享
Book学术官方微信
Book学术文献互助
Book学术文献互助群
群 号:481959085
Book学术
文献互助 智能选刊 最新文献 互助须知 联系我们:info@booksci.cn
Book学术提供免费学术资源搜索服务,方便国内外学者检索中英文文献。致力于提供最便捷和优质的服务体验。
Copyright © 2023 Book学术 All rights reserved.
ghs 京公网安备 11010802042870号 京ICP备2023020795号-1