{"title":"基于深度学习的相位调制全息数据存储的图像分割","authors":"Ruixian Chen, Jinyu Wang, Shaodong Zhang, Rongquan Fan, Dakui Lin, Xiong Li, Jihong Zheng, Qiang Cao, Jianying Hao, Xiao Lin, Xiaodi Tan","doi":"10.1364/oe.536783","DOIUrl":null,"url":null,"abstract":"Phase retrieval based on data-driven deep learning (DL) is a suitable decoding method for phase-modulated holographic data storage (HDS). Once the DL network is trained, the phase can be directly retrieved from the corresponding diffraction intensity image with high data transfer rate and low bit error rate. Traditional data-driven DL-based phase retrieval requires a large number of known samples for training, which is usually laborious for practical applications such as HDS. In the paper, we propose an image segmentation method based on image features, leading to about 54 times reduction in the number of original sample pairs (OSP) for training DL network. The proposed method is easy to implement in practical situations of HDS.","PeriodicalId":19691,"journal":{"name":"Optics express","volume":"24 1","pages":""},"PeriodicalIF":3.2000,"publicationDate":"2024-09-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"Image segmentation of phase-modulated holographic data storage based on deep learning\",\"authors\":\"Ruixian Chen, Jinyu Wang, Shaodong Zhang, Rongquan Fan, Dakui Lin, Xiong Li, Jihong Zheng, Qiang Cao, Jianying Hao, Xiao Lin, Xiaodi Tan\",\"doi\":\"10.1364/oe.536783\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"Phase retrieval based on data-driven deep learning (DL) is a suitable decoding method for phase-modulated holographic data storage (HDS). Once the DL network is trained, the phase can be directly retrieved from the corresponding diffraction intensity image with high data transfer rate and low bit error rate. Traditional data-driven DL-based phase retrieval requires a large number of known samples for training, which is usually laborious for practical applications such as HDS. In the paper, we propose an image segmentation method based on image features, leading to about 54 times reduction in the number of original sample pairs (OSP) for training DL network. The proposed method is easy to implement in practical situations of HDS.\",\"PeriodicalId\":19691,\"journal\":{\"name\":\"Optics express\",\"volume\":\"24 1\",\"pages\":\"\"},\"PeriodicalIF\":3.2000,\"publicationDate\":\"2024-09-09\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Optics express\",\"FirstCategoryId\":\"101\",\"ListUrlMain\":\"https://doi.org/10.1364/oe.536783\",\"RegionNum\":2,\"RegionCategory\":\"物理与天体物理\",\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"Q2\",\"JCRName\":\"OPTICS\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Optics express","FirstCategoryId":"101","ListUrlMain":"https://doi.org/10.1364/oe.536783","RegionNum":2,"RegionCategory":"物理与天体物理","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q2","JCRName":"OPTICS","Score":null,"Total":0}
Image segmentation of phase-modulated holographic data storage based on deep learning
Phase retrieval based on data-driven deep learning (DL) is a suitable decoding method for phase-modulated holographic data storage (HDS). Once the DL network is trained, the phase can be directly retrieved from the corresponding diffraction intensity image with high data transfer rate and low bit error rate. Traditional data-driven DL-based phase retrieval requires a large number of known samples for training, which is usually laborious for practical applications such as HDS. In the paper, we propose an image segmentation method based on image features, leading to about 54 times reduction in the number of original sample pairs (OSP) for training DL network. The proposed method is easy to implement in practical situations of HDS.
期刊介绍:
Optics Express is the all-electronic, open access journal for optics providing rapid publication for peer-reviewed articles that emphasize scientific and technology innovations in all aspects of optics and photonics.