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Experimental Study of Ghost Imaging in Underwater Environment.
Wu, Heng; Chen, Ziyan; He, Chunhua; Cheng, Lianglun; Luo, Shaojuan.
Afiliação
  • Wu H; Guangdong Provincial Key Laboratory of Cyber-Physical System, School of Automation, Guangdong University of Technology, Guangzhou 510006, China.
  • Chen Z; School of Computer, Guangdong University of Technology, Guangzhou 510006, China.
  • He C; Guangdong Provincial Key Laboratory of Cyber-Physical System, School of Automation, Guangdong University of Technology, Guangzhou 510006, China.
  • Cheng L; Guangdong Provincial Key Laboratory of Cyber-Physical System, School of Automation, Guangdong University of Technology, Guangzhou 510006, China.
  • Luo S; School of Computer, Guangdong University of Technology, Guangzhou 510006, China.
Sensors (Basel) ; 22(22)2022 Nov 18.
Article em En | MEDLINE | ID: mdl-36433544
ABSTRACT
Underwater imaging technique is a crucial tool for humans to develop, utilize, and protect the ocean. We comprehensively compare the imaging performance of twenty-four ghost imaging (GI) methods in the underwater environment. The GI methods are divided into two types according to the illumination patterns, the random and orthogonal patterns. Three-group simulations were designed to show the imaging performance of the twenty-four GI methods. Moreover, an experimental system was built, and three-group experiments were implemented. The numerical and experimental results demonstrate that the orthogonal pattern-based compressed sensing GI methods have strong antinoise capability and can restore clear images for underwater objects with a low measurement number. The investigation results are helpful for the practical applications of the underwater GI.
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Texto completo: 1 Coleções: 01-internacional Base de dados: MEDLINE Assunto principal: Diagnóstico por Imagem Tipo de estudo: Diagnostic_studies Limite: Humans Idioma: En Revista: Sensors (Basel) Ano de publicação: 2022 Tipo de documento: Article País de afiliação: China

Texto completo: 1 Coleções: 01-internacional Base de dados: MEDLINE Assunto principal: Diagnóstico por Imagem Tipo de estudo: Diagnostic_studies Limite: Humans Idioma: En Revista: Sensors (Basel) Ano de publicação: 2022 Tipo de documento: Article País de afiliação: China