The underwater polarization dehazing imaging has attracted a lot of interest due to the potential applications in corresponding fields. There are some progress on the underwater polarization dehazing imaging by introducing the deep learning into polarization dehazing imaging. In this work, the underwater active polarization dehazing imaging based on the deep learning model is studied. A modified All-in-One Dehazing Network model with three input channels is designed under the framework of TensorFlow. The polarization image data of three different polarization components are designed as the training set with the convolution neural network (CNN).This light-weight CNN is designed to achieve underwater dehazing imaging of different targets with different turbidity. Experiment results indicate that the prediction and estimation using modified AOD-Net have better accuracy than that of the traditional dehazing model.
The underwater optical polarization imaging with active light source illumination is studied. The mechanism and modeling of active polarization detection process with the linear polarization are demonstrated through theoretical and experimental analysis. The corresponding experiments were carried out for different objects with different distances and materials in water with different turbidity. In particular, the imaging effects with different wavelengths of light sources were compared at low turbidity, the imaging effects with 532 nm wavelength light are better than that of 633 nm wavelength. These results may benefit the underwater optical polarization imaging for achieving more diversified and flexible functions.
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