Optical signal detection in turbid water using multidimensional integral imaging with deep learning.

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Abstract

Optical signal detection in turbid and occluded environments is a challenging task due to the light scattering and beam attenuation inside the medium. Three-dimensional (3D) integral imaging is an imaging approach which integrates two-dimensional images from multiple perspectives and has proved to be useful for challenging conditions such as occlusion and turbidity. In this manuscript, we present an approach for the detection of optical signals in turbid water and occluded environments using multidimensional integral imaging employing temporal encoding with deep learning. In our experiments, an optical signal is temporally encoded with gold code and transmitted through turbid water via a light-emitting diode (LED). A camera array captures videos of the optical signals from multiple perspectives and performs the 3D signal reconstruction of temporal signal. The convolutional neural network-based bidirectional Long Short-Term Network (CNN-BiLSTM) network is trained with clear water video sequences to perform classification on the binary transmitted signal. The testing data was collected in turbid water scenes with partial signal occlusion, and a sliding window with CNN-BiLSTM-based classification was performed on the reconstructed 3D video data to detect the encoded binary data sequence. The proposed approach is compared to previously presented correlation-based detection models. Furthermore, we compare 3D integral imaging to conventional two-dimensional (2D) imaging for signal detection using the proposed deep learning strategy. The experimental results using the proposed approach show that the multidimensional integral imaging-based methodology significantly outperforms the previously reported approaches and conventional 2D sensing-based methods. To the best of our knowledge, this is the first report on underwater signal detection using multidimensional integral imaging with deep neural networks.