Underwater Polarization Imaging Recovery Based on Polarimetric Residual Dense Network
Yanfa Xiang, Xu Yang, Qiming Ren, Guochen Wang, Jie Gao, Khian‐Hooi Chew, Rui‐Pin Chen
Abstract
Application of deep-learning to polarization imaging technology for image restoration has led to many technological breakthroughs, especially in underwater image recovery and recognition. In this work, a four-input deep learning model with the Polarimetric Residual Dense Network is proposed for underwater image recovery. The diverse polarization component images are trained and tested in different processes in the network for the recognition and dehazing by considering the physical model of polarization dehazing imaging. Our study reveals that the proposed method can efficiently recover the hazed images, and provide good performance for improving the quality of image restoration even in a high-turbidity complex underwater environment.