Affiliation:
1. School of Computer Science and Engineering Wuhan Institute of Technology Wuhan 430205 China
2. Hubei Key Laboratory of Intelligent Robot Wuhan Institute of Technology Wuhan 430205 China
Abstract
Nematic liquid‐crystal microlens arrays (LC‐MLAs) often exhibit chromatic aberration and low resolution, severely compromising their optical imaging quality. This study proposes an achromatic and resolution enhancement light field (ARELF) deep neural network (DNN) to address these issues. The training set is constructed by incorporating LC‐MLA characteristics’ degradation, retrofitting the vimeo90k dataset. The network's hidden layer is trained to learn about chromatic aberration and low resolution of LC‐MLA while extracting imaging features and fusing the information of complementary features of a light field under varying voltages. The loss function includes both chromatic aberration and overall resolution. The light field images of ZnO LC‐MLA under seven consecutive voltages are used as input to test the proposed DNN model. After experimental verification, the proposed model effectively eliminates chromatic aberration while enhancing the spatial and temporal resolution of LC‐MLA. This novel network can be utilized to optimize the design process of LC‐MLA and significantly improve its imaging performance.
Funder
National Natural Science Foundation of China
Hubei Provincial Key Laboratory of Intelligent Robot
Hubei Provincial Department of Education
Foundation for Innovative Research Groups of Hubei Province of China
Subject
Pharmacology (medical),Complementary and alternative medicine,Pharmaceutical Science
Cited by
2 articles.
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