Abstract
Even though ghost imaging (GI), an unconventional imaging method, has received increased attention by researchers during the last decades, imaging speed is still not satisfactory. Once the data-acquisition method and the system parameters are determined, only the processing method has the potential to accelerate image-processing significantly. However, both the basic correlation method and the compressed sensing algorithm, which are often used for ghost imaging, have their own problems. To overcome these challenges, a novel deep learning ghost imaging method is proposed in this paper. We modified the convolutional neural network that is commonly used in deep learning to fit the characteristics of ghost imaging. This modified network can be referred to as ghost imaging convolutional neural network. Our simulations and experiments confirm that, using this new method, a target image can be obtained faster and more accurate at low sampling rate compared with conventional GI method.
Highlights
Ghost imaging (GI) is a relatively new imaging method compared with conventional imaging methods
Our imaging configuration is based on the CGI framework[8], as shown in Fig. 2, which consists of a target, a Digital Micro-mirror Device (DMD), and a bucket detector
The DMD projects a sequence of 64*64 random speckles onto the target, and the reflected light is detected by the CCD that gives the echo signals
Summary
Ghost imaging (GI) is a relatively new imaging method compared with conventional imaging methods. The goal of our technique is to reconstruct the target image quickly and accurately at low sampling rate. A novel deep learning ghost imaging (DLGI) method is proposed that is consistent with the GI principle. The CS algorithm has a large computational challenge to acquire an ideal target image, it can reconstruct a rudimentary target image quickly at low sampling rate. Mousavi et al proposed a fast reconstruction algorithm based on deep CNN network, which can reconstruct the original image with fewer sample points. The target image can be quickly and accurately reconstructed at low sampling rate, when testing. A novel deep learning ghost imaging method is proposed, which has advantages both in calculation speed and image accuracy.
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