The patent badge is an abbreviated version of the USPTO patent document. The patent badge does contain a link to the full patent document.
The patent badge is an abbreviated version of the USPTO patent document. The patent badge covers the following: Patent number, Date patent was issued, Date patent was filed, Title of the patent, Applicant, Inventor, Assignee, Attorney firm, Primary examiner, Assistant examiner, CPCs, and Abstract. The patent badge does contain a link to the full patent document (in Adobe Acrobat format, aka pdf). To download or print any patent click here.
Patent No.:
Date of Patent:
Jun. 21, 2022
Filed:
Jan. 18, 2018
South China Normal University, Guangzhou, CN;
Jun Li, Guangzhou, CN;
Miao Lei, Shenzhen, CN;
Xiaofang Dai, Guangzhou, CN;
Shangyuan Wang, Guangzhou, CN;
Ting Zhong, Guangzhou, CN;
Chuangxue Liang, Guangzhou, CN;
Chen Wang, Nanjing, CN;
Ping Xie, Guangzhou, CN;
Ruiqiang Wang, Guangzhou, CN;
SOUTH CHINA NORMAL UNIVERSITY, Guangzhou, CN;
Abstract
A compressed sensing based object imaging system and an imaging method thereof. The object imaging system comprises a light source generation unit (), a filter unit (), an image generation unit (), an image acquisition unit (), and an image reconstruction unit (). The light source generation unit () generates experimental laser; the filter unit () filters high frequency scattered light and forms parallel light; the image generation unit () generates an experimental image in which an object image () and a specific measurement matrix () are superimposed; the image acquisition unit () performs compression sampling on the generated experimental image; and the image reconstruction unit () reconstructs sampling data to restore the object image (). The imaging method comprises: establishing a sample database comprising the specific target object image (); training sample images to obtain the specific measurement matrix (); and simultaneously completing image sampling, image compression and image recognition in an all-optical system. The system and the method can greatly reduce the data volume recorded in image recognition and image matching, thus improving the real-time performance of the system, and providing a possibility of concurrent processing by machine vision and artificial intelligence.