Tensorflow implementation of paper: Far-field super-resolution ghost imaging with a deep neural network constraint.. One of the experiment data was provided.
If you find this project useful, we would be grateful if you cite the GIDC paper：
Fei Wang, Chenglong Wang, Mingliang Chen, Wenlin Gong, Yu Zhang, Shensheng Han and Guohai Situ. Far-field super-resolution ghost imaging with a deep neural network constraint. Light Sci Appl 11, 1 (2022).
Ghost imaging (GI) facilitates image acquisition under low-light conditions by single-pixel measurements and thus has great potential in applications in various fields ranging from biomedical imaging to remote sensing. However, GI usually requires a large amount of single-pixel samplings in order to reconstruct a high-resolution image, imposing a practical limit for its applications. Here we propose a far-field super-resolution GI technique that incorporates the physical model for GI image formation into a deep neural network. The resulting hybrid neural network does not need to pre-train on any dataset, and allows the reconstruction of a far-field image with the resolution beyond the diffraction limit. Furthermore, the physical model imposes a constraint to the network output, making it effectively interpretable. We experimentally demonstrate the proposed GI technique by imaging a flying drone, and show that it outperforms some other widespread GI techniques in terms of both spatial resolution and sampling ratio. We believe that this study provides a new framework for GI, and paves a way for its practical applications.
Step 1: Configuring required packages
python 3.6
tensorflow 1.9
matplotlib 3.1.3
numpy 1.18.1
pillow 7.1.2
Step 2: Run GIDC_main.py after download and extract the ZIP file.
For academic and non-commercial use only.