An efficient neural network for low sampling computational ghost imaging based on EMNIST training. Issue 19 (11th November 2022)

• Record Type: Journal Article
• Authors: Chen, Xu; Wang, Chunfang; Zhao, Quanchao
• Is Part Of: Journal of modern optics. Volume 69:Issue 19(2022)
• Page Start: 1079
• Page End: 1085
• Publication Date: 2022-11-11
• Subjects: Ghost imaging -- single-pixel imaging -- deep learning -- low sampling; Optics -- Periodicals; 535
• DOI: 10.1080/09500340.2022.2138594
• Languages: English
• ISSNs: 0950-0340

Abstract

Abstract : In this paper, we propose a new network to achieve low-sampling computational ghost imaging. The proposed neural network that combines ResNeXt, eHoloNet and spatial attention mechanism is efficient for object reconstruction just based on EMNIST training, which is much simpler and time-saving. The generalization of the neural network is verified with multi-slits as well as complex object. Both simulated and experimental results show that the proposed network can give an effective reconstruction at 0.7% sampling rate. The neural network in this work is of great significance to ghost imaging in a wider application scenarios, such as real-time imaging and dynamic detection of motion object.