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.