# Attention-Based Mechanism and Adversarial Autoencoder for Underwater Image Enhancement
## 1 Introduction
In recent years, considerable attention has been given to the field of deep learning, as deep learning neural networks demonstrate remarkable capabilities in solving complex nonlinear system modeling problems. This has led to significant advancements in the enhancement of underwater images. For instance, [PERSON] et al. [13] proposed the UWCNN model, which utilizes distinct neural network models tailored to different types of underwater images. [PERSON] et al. [14] introduced the UIEC\({}^{\prime\prime}\)2-Net, which incorporates RGB and HSV color spaces, as well as an attention module, to enhance underwater images. Additionally, [PERSON] et al. [15] proposed the Ucolor network, which is based on a multi-color space approach. They introduced reverse medium transmission (RMT) images into the network as weight information to guide the enhancement of underwater RGB images. In a separate work, [PERSON] et al. [16] presented the innovative WaterGAN model, a type of Generative Adversarial Network [17] (GAN). This model generates underwater images by taking atmospheric RGB images, depth maps, and noise vectors as inputs. Subsequently, a convolutional neural network is trained using the synthesized images, atmospheric images, and depth maps to achieve the enhancement of target images. However, it should be noted that the WaterGAN model inherits the inherent drawback of GAN-based models, namely the production of unstable enhancement results. While these methods can enhance the visual quality of underwater images to a certain extent, they heavily rely on neural networks to learn parameters and achieve enhanced images through nonlinear mapping. However, this approach faces difficulties in effectively capturing the characteristics and laws of underwater optical imaging in complex underwater environments. Consequently,the reliability of the results is compromised, making it challenging to address the problem of deteriorating image quality in different underwater scenes. The current deep learning-based models for underwater image enhancement exhibit limited robustness and generalization ability, which is unsatisfactory. This limitation stems from the fact that while deep learning-based methods circumvent the complex physical parameter estimation required by traditional approaches, they often overlook the domain knowledge specific to underwater imaging. To address this issue, we propose an underwater image enhancement method based on Adversarial Autoencoder (AAE), which combines the characteristics of underwater imaging. The method integrates features extracted from three color spaces of the image (RGB, HSV, and Lab) into a unified structure to enhance the diversity of feature representations. Additionally, the attention mechanism is employed to capture rich features. Secondly, the extracted features mentioned above are fused with the features obtained from the Reverse Medium Transmission (RMT) map using a feature fusion module. This fusion process enhances the network's ability to respond to regions in the image that have experienced degradation in terms of image quality. Finally, the discriminator enhances the reconstructed image by accepting positive samples from the pre-trained model and negative samples generated by the encoder (generator). Through this process, the discriminator guides the hidden space of the autoencoder to approach the hidden space of the pre-trained model, contributing to the overall improvement of the image quality. In this paper, the proposed method demonstrates excellent performance on an experimental dataset collected from the \"Offshore Oil Platform HYSY-163\" situated in the Beibu Gulf area of China. Figure 1 illustrates the results of the original image and the processed image using the three color channels (R, G, and B). The reconstructed image exhibits a more uniform distribution in the histogram and possesses enhanced clarity. The remaining sections of the paper are organized as follows. Section 2 presents the details of our proposed method for underwater image enhancement tasks.