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\begin{document}

\title{An Underwater Image Enhancement Model Combining Physical Priors and
Residual Network}



\author[1]{Xinnan Fan}%
\author[2]{Xuan Zhou}%
\author[1]{Hongzhu Chen}%
\author[3]{Yuanxue Xin}%
\author[4]{Pengfei Shi}%
\affil[1]{Hohai University}%
\affil[2]{Hohai University - Changzhou Campus}%
\affil[3]{College of Information Science and Engineering, Hohai University}%
\affil[4]{Hohai University College of Internet of Things Engineering}%


\vspace{-1em}



  
  \date{May 14, 2024}


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\begin{abstract}
Considering light absorbing and scattering problems in connection with
wavelength can decrease the visibility, contrast and color distortion of
images, we propose a new type of convolutional neural network with two
training phases. Firstly, the coordinate attention module is integrated
into the residual block of the residual group in the backbone network,
which is used to strengthen the feature extraction capability of the
network. Secondly, since the unrealistic image colors may degrade the
image details, an unsupervised method that combines the physical prior
knowledge and the real underwater images is proposed to finetune the
backbone network. Furthermore, a model protection mechanism is designed
to guarantee the successful execution of the training. The experimental
results indicate the proposed model can effectively optimize the
contrast, color and image quality of the underwater image. Compared with
relevant algorithms, our UCIQE and NIQE are respectively 0.525 and
4.149, which further verifies the superiority of the proposed model.%
\end{abstract}%



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