Jue Lyu , Dong-Jie Bi , Bo Liu , Guo Yi , Xue-Peng Zheng , Xi-Feng Li , Li-Biao Peng , Yong-Le Xie , Yi-Ming Zhang , Ying-Li Bai
{"title":"基于基尼指数和总变分混合正则化的压缩毫米波近场成像算法","authors":"Jue Lyu , Dong-Jie Bi , Bo Liu , Guo Yi , Xue-Peng Zheng , Xi-Feng Li , Li-Biao Peng , Yong-Le Xie , Yi-Ming Zhang , Ying-Li Bai","doi":"10.1016/j.jnlest.2023.100191","DOIUrl":null,"url":null,"abstract":"<div><p>A compressive near-field millimeter wave (MMW) imaging algorithm is proposed. From the compressed sensing (CS) theory, the compressive near-field MMW imaging process can be considered to reconstruct an image from the under-sampled sparse data. The Gini index (GI) has been founded that it is the only sparsity measure that has all sparsity attributes that are called Robin Hood, Scaling, Rising Tide, Cloning, Bill Gates, and Babies. By combining the total variation (TV) operator, the GI-TV mixed regularization introduced compressive near-field MMW imaging model is proposed. In addition, the corresponding algorithm based on a primal-dual framework is also proposed. Experimental results demonstrate that the proposed GI-TV mixed regularization algorithm has superior convergence and stability performance compared with the widely used <em>l</em><sub>1</sub>-TV mixed regularization algorithm.</p></div>","PeriodicalId":53467,"journal":{"name":"Journal of Electronic Science and Technology","volume":"21 1","pages":"Article 100191"},"PeriodicalIF":0.0000,"publicationDate":"2023-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"1","resultStr":"{\"title\":\"Compressive near-field millimeter wave imaging algorithm based on gini index and total variation mixed regularization\",\"authors\":\"Jue Lyu , Dong-Jie Bi , Bo Liu , Guo Yi , Xue-Peng Zheng , Xi-Feng Li , Li-Biao Peng , Yong-Le Xie , Yi-Ming Zhang , Ying-Li Bai\",\"doi\":\"10.1016/j.jnlest.2023.100191\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"<div><p>A compressive near-field millimeter wave (MMW) imaging algorithm is proposed. From the compressed sensing (CS) theory, the compressive near-field MMW imaging process can be considered to reconstruct an image from the under-sampled sparse data. The Gini index (GI) has been founded that it is the only sparsity measure that has all sparsity attributes that are called Robin Hood, Scaling, Rising Tide, Cloning, Bill Gates, and Babies. By combining the total variation (TV) operator, the GI-TV mixed regularization introduced compressive near-field MMW imaging model is proposed. In addition, the corresponding algorithm based on a primal-dual framework is also proposed. Experimental results demonstrate that the proposed GI-TV mixed regularization algorithm has superior convergence and stability performance compared with the widely used <em>l</em><sub>1</sub>-TV mixed regularization algorithm.</p></div>\",\"PeriodicalId\":53467,\"journal\":{\"name\":\"Journal of Electronic Science and Technology\",\"volume\":\"21 1\",\"pages\":\"Article 100191\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2023-03-01\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"1\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Journal of Electronic Science and Technology\",\"FirstCategoryId\":\"95\",\"ListUrlMain\":\"https://www.sciencedirect.com/science/article/pii/S1674862X23000095\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"Q1\",\"JCRName\":\"Engineering\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Journal of Electronic Science and Technology","FirstCategoryId":"95","ListUrlMain":"https://www.sciencedirect.com/science/article/pii/S1674862X23000095","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q1","JCRName":"Engineering","Score":null,"Total":0}
Compressive near-field millimeter wave imaging algorithm based on gini index and total variation mixed regularization
A compressive near-field millimeter wave (MMW) imaging algorithm is proposed. From the compressed sensing (CS) theory, the compressive near-field MMW imaging process can be considered to reconstruct an image from the under-sampled sparse data. The Gini index (GI) has been founded that it is the only sparsity measure that has all sparsity attributes that are called Robin Hood, Scaling, Rising Tide, Cloning, Bill Gates, and Babies. By combining the total variation (TV) operator, the GI-TV mixed regularization introduced compressive near-field MMW imaging model is proposed. In addition, the corresponding algorithm based on a primal-dual framework is also proposed. Experimental results demonstrate that the proposed GI-TV mixed regularization algorithm has superior convergence and stability performance compared with the widely used l1-TV mixed regularization algorithm.
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