# Attention-Based Mechanism and Adversarial Autoencoder for Underwater Image Enhancement
## 3 Experimentation and Analysis
### Analysis of Objective Indicators  
#### 3.3.1 Objective Metrics Analysis on Test Set A  
To further validate the effectiveness of our proposed method, two non-reference image evaluation metrics, namely Natural Image Quality Evaluator [30] (NIQE) and Underwater Color Image Quality Evaluation [31] (UCIQE), were used to objectively analyze and compare the enhancement effects of the aforementioned seven algorithms on the images from test set A.  
NIQE is a non-reference image quality evaluation metric based on Multivariate Gaussian (MVG) modeling. It involves obtaining a collection of features from the original image using a highly regular Natural Scene Statistic (NSS) model. These features are then fitted to an MVG model. In the underwater image evaluation process, the quality of the image being evaluated is expressed as the distance between the multivariate Gaussian fitting parameters of the NSS features extracted from the image and the parameters of the image model being evaluated. A lower value of NIQE indicates higher quality of the enhanced image. It is computed as follows:  
\[D(v_{1},v_{2},\Sigma_{1},\Sigma_{2})=\sqrt{\Bigg{(}(v_{1}-v_{2})^{\text{T}} \Bigg{(}\frac{\Sigma_{1}+\Sigma_{2}}{2}\Bigg{)}^{-1}(v_{1}-v_{2})\Bigg{)}} \tag{16}\]  
where \(v_{1}\), \(v_{2}\), \(\Sigma_{1}\), and \(\Sigma_{2}\) represent the mean vector and covariance matrix of the natural MVG model and the distorted image MVG model, respectively. Figure 10: Visual comparison of different types of images (from test set B) enhanced by six state-of-the-art algorithms with our proposed approach. The underwater color image quality evaluation metric UCIQE, used for images in the CIELab space, is a linear combination of underwater image color concentration, saturation, and contrast. It is designed to quantitatively assess the non-uniform color bias, blurriness, and low contrast of an image, and is defined as follows:  
\[\text{UCIQE}=c_{1}\times\sigma_{c}+c_{2}\times\mu_{s}+c_{3}\times\text{con}_{l} \tag{17}\]  
where \(\sigma_{c}\) represents the standard deviation of chromaticity, \(\text{con}_{l}\) denotes the contrast of luminance, and \(\mu_{s}\) is the average value of saturation. The parameters \(c_{1}\), \(c_{2}\), and \(c_{3}\) are the weighting coefficients, which are set to 0.4860, 0.2576, and 0.2745, respectively. A higher value of UCIQE indicates that the image contains more details, and a more desirable enhancement effect has been achieved. As observed in Table 1, our method demonstrates a substantial improvement in the reconstructed images, with an average reduction of 27.8% in the NIQE value compared to the unprocessed real underwater images. The average NIQE value is lower than that of the other six algorithms. Lower NIQE values correspond to higher image quality, which suggests that the reconstructed image quality is the most desirable among all the evaluated effects. As depicted in Table 2, our method shows a notable improvement in the processed images, with an average increase of more than 28.8% in the UCIQE value compared to the unprocessed real underwater images. This increase indicates a significant enhancement in color concentration, saturation, and contrast, suggesting that the images have been significantly improved in these aspects. However, it is worth noting that the average score is lower than that of some traditional algorithms, and the excessively high UCIQE scores may result from the image exhibiting phenomena of oversaturation and unnatural effects, such as those produced by the HE algorithm. In summary, our method demonstrates strong performance in visualization and achieves superior enhancement of underwater images.