Ghost imaging with probability estimation using convolutional neural network: improving estimation accuracy using parallel convolutional neural network

In demand for minute defect inspection, it is required to detect weak scattered light caused by small defects. Ghost imaging (GI) is known for its high sensitivity and high noise resistance method. However, it requires many measurements to obtain a high-quality image because GI is the correlation-based imaging method. Reducing the number of measurements, a method combined with deep learning has been proposed. In order to improve the estimation accuracy using CNN, we propose to parallelize the convolutional layers. Parallel convolutional layers can efficiently extract both local and global features, which contributes to the improvement of estimation accuracy. In this report, we show that parallel CNN is more accurate than conventional CNN by experiments.