A neural network for underwater polarization dehazing imaging

Yanfa Xiang; Qiming Ren; Rui-Pin Chen

doi:10.1117/12.2602463

9 October 2021 A neural network for underwater polarization dehazing imaging

Yanfa Xiang, Qiming Ren, Rui-Pin Chen

Proceedings Volume 11897, Optoelectronic Imaging and Multimedia Technology VIII; 1189702 (2021) https://doi.org/10.1117/12.2602463
Event: SPIE/COS Photonics Asia, 2021, Nantong, JS, China

Abstract

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.

Conference Presentation

Citation Download Citation

Yanfa Xiang, Qiming Ren, and Rui-Pin Chen "A neural network for underwater polarization dehazing imaging", Proc. SPIE 11897, Optoelectronic Imaging and Multimedia Technology VIII, 1189702 (9 October 2021); https://doi.org/10.1117/12.2602463

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