# A Survey of Target Detection and Recognition Methods in Underwater Turbid Areas
## 4 Research on Target Detection and Recognition in Turbid Waters
### Underwater Image Restoration and Enhancement Methods
Regarding the enhancement of the quality of underwater images taken in different water body types, the image forming model used in earlier methods is imprecise and its restoration effect is poor. [PERSON] et al. [37] developed a defogging method using a modified model. They first designed an underwater image depth estimation method to create depth maps and estimate backscattering based on the depth values of each pixel, and then removed backscattering based on a more accurate underwater imaging model. To address the color distortion characteristics of the turbid area, they proposed a color correction method to automatically adjust the global color distribution of an image. This method used a single underwater image as the input, eliminating the effects of light wave absorption and scattering. Experiments have demonstrated that this approach has better applicability compared with previous research methods. Considering that scattering attenuation and color correction of high-turbidity underwater images affects the classification results of target recognition based on machine learning (ML), [PERSON] et al. [38] proposed a contrast-enhanced method to remove scattering. This enhancement method considers the illumination and camera spectral characteristics, eliminates scattering, and correctly restores the scene color. They also used different ML approaches for classification in their research to confirm that this method can be applied to classification and recognition architecture preprocessing based on deep learning, which showed a better image classification effect. However, for practical applications, use of the scattering removal algorithm does not provide the accuracy required by practical engineering. [PERSON] [39] proposed an underwater polarized imaging target enhancement technique based on non-polarized illumination to overcome the disadvantages of the current underwater polarized scattering algorithm, such as its low accuracy and limited application range. The use of unpolarized light ensures that any polarization difference between the target reflected light and stray light can be detected. At the same time, the characteristic parameter of the polarization angle ensures accurate estimation of the stray light intensity. Compared with current underwater polarized imaging technology based on linear polarized light illumination, it has a wider application range and higher image restoration accuracy. The results showed that the visibility of underwater restored images is improved effectively, and the contrast is improved by at least 100%. Meanwhile, this technique can be applied to water environments with various material targets, imaging distances, diverse impurities, and turbidity levels, and has potential application value in many underwater imaging fields. [PERSON] et al. [40] proposed a new underwater restoration method based on monocular image sequences, which utilizes the time relationship and geometric and environmental information to improve the quality of visual features in underwater images. It can also \begin{table}
\begin{tabular}{c c c} \hline \hline
**Parameters** & **Existing Approach (Transmission & **Proposed Approach** \\  & **Map Estimation)**[35] & **(UD-ETR-Based Restoration)** \\ \hline Contract luminance & 39 & 89 \\ UCIQE & 12 & 26 \\ Saturation & 0.1 & 0.5 \\ Chroma & 2.5 & 5.5 \\ PSNR & 5 & 14 \\ RMSE & 140 & 50 \\ MSE & 1.7 & 0.3 \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Comparison of the parameters from transmission map estimation and UD-ETR. robustly estimate the depth map and attenuation coefficient. The attenuation coefficient is used to evaluate the loss of light in the medium, so the accuracy of its estimation affects image restoration. Depth estimation is realized using adaptive optical flow and structure motion technology, and the attenuation coefficient is estimated by introducing an underwater optical attenuation model into the RANSAC frame [41]. Meanwhile, a depth map is estimated from the combination of motion structure technology and model-based restoration.