Scientific Reports· 2017Q1
Deep-learning-based ghost imaging
- 440citations
- Q1SCImago
- 2017year
Short summary
A novel framework called Ghost Imaging using Deep Learning (GIDL) reconstructs higher quality images from fewer samples than traditional methods.
AI-generated from the title and abstract; the full text is not read.
Key points
- Introduces Ghost Imaging using Deep Learning (GIDL) framework.
- Deep neural network learns sensing model from training data.
- Achieves better image reconstruction quality than traditional methods.
- Outperforms compressive sensing at extremely low sampling rates.
AI-generated from the title and abstract; the full text is not read.
Abstract
In this manuscript, we propose a novel framework of computational ghost imaging, i.e., ghost imaging using deep learning (GIDL). With a set of images reconstructed using traditional GI and the corresponding ground-truth counterparts, a deep neural network was trained so that it can learn the sensing model and increase the quality image reconstruction. Moreover, detailed comparisons between the image reconstructed using deep learning and compressive sensing shows that the proposed GIDL has a much better performance in extremely low sampling rate. Numerical simulations and optical experiments were carried out for the demonstration of the proposed GIDL.
The authors' abstract, as published at the source. Scientific Reports, 2017 · DOI ↗
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Field: Acoustics and Ultrasonics
Acoustics and UltrasonicsPhysics and Astronomy