Imaging through turbid media with vague concentrations based on cosine similarity and convolutional neural network

Abstract

Underwater imaging has been extensively studied to bypass the limitation aroused by scattering and absorption of water solutions. It is highly meaningful to the development of optical imaging, especially in turbid media. The existing methods developed for reconstruction of original images from speckle patterns are applied in a stable medium, which obstruct wider applications in unpredictable media. Hence, it is crucial to take changeable environments into consideration to circumvent the limits of the extant methods. In this paper, we propose a new approach based on cosine similarity for speckle classification and convolutional neural network (CNN) for the reconstruction. The targets are placed in variant densities of turbid water mixed with certain of milk, and their corresponding intensity speckle patterns are recorded by a camera. It is verified that utilization of cosine similarity for the classification of patterns recorded in changeable media ensures high fidelity for label predictions. For a speckle pattern obtained in totally unidentified media, it can make a prediction of the density which has a high probability of accuracy. We can exploit the classified density to automatically select the most appropriate datasets to train a CNN model and then make predictions in real time with the trained CNN model. The combined model presented in this paper is tolerant to the uncertainty of turbidity. Moreover, it guarantees high-accuracy pattern classification and high-quality image reconstruction. It is feasible for potential applications in harsh water solutions with unknown perturbations of concentrations.