首页|The underwater polarization dehazing imaging with a lightweight convolutional neural network
The underwater polarization dehazing imaging with a lightweight convolutional neural network
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NSTL
Elsevier
The scattering and absorption of particles in underwater environment seriously affect the quality of underwater images, resulting in reduced contrast and imaging quality. In this work, the underwater active polarization dehazing imaging based on the deep learning model is studied. A modified lightweight dehazing convolutional neural network (CNN) model with four input channels is designed by combining both the advantages of the deep learning and polarization dehazing imaging technology. The lightweight CNN is trained and tested with the images of different polarization components (00, 450, 900 linear polarization and circular polarization) in different turbidity underwater environments. The experimental results show that this method can rapidly achieve the better dehazing imaging effect than that of conventional dehazing methods.
NSTL
Elsevier
