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Deep Learning Enables Optofluidic Zoom System with Large Zoom Ratio and High Imaging Resolution.
Xu, Jiancheng; Kuang, Fenglin; Liu, Shubin; Li, Lei.
Affiliation
  • Xu J; School of Electronics and Information Engineering, Sichuan University, Chengdu 610065, China.
  • Kuang F; School of Electronics and Information Engineering, Sichuan University, Chengdu 610065, China.
  • Liu S; School of Electronics and Information Engineering, Sichuan University, Chengdu 610065, China.
  • Li L; School of Electronics and Information Engineering, Sichuan University, Chengdu 610065, China.
Sensors (Basel) ; 23(6)2023 Mar 17.
Article in En | MEDLINE | ID: mdl-36991915
ABSTRACT
Due to the relatively low optical power of a liquid lens, it is usually difficult to achieve a large zoom ratio and a high-resolution image simultaneously in an optofluidic zoom imaging system. We propose an electronically controlled optofluidic zoom imaging system combined with deep learning, which achieves a large continuous zoom change and a high-resolution image. The zoom system consists of an optofluidic zoom objective and an image-processing module. The proposed zoom system can achieve a large tunable focal length range from 4.0 mm to 31.3 mm. In the focal length range of 9.4 mm to 18.8 mm, the system can dynamically correct the aberrations by six electrowetting liquid lenses to ensure the image quality. In the focal length range of 4.0-9.4 mm and 18.8-31.3 mm, the optical power of a liquid lens is mainly used to enlarge the zoom ratio, and deep learning enables the proposed zoom system with improved image quality. The zoom ratio of the system reaches 7.8×, and the maximum field of view of the system can reach ~29°. The proposed zoom system has potential applications in camera, telescope and so on.
Key words

Full text: 1 Collection: 01-internacional Database: MEDLINE Language: En Journal: Sensors (Basel) Year: 2023 Document type: Article Affiliation country: China

Full text: 1 Collection: 01-internacional Database: MEDLINE Language: En Journal: Sensors (Basel) Year: 2023 Document type: Article Affiliation country: China