# 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  
Deep learning has been widely used in underwater image restoration and enhancement to improve the quality of underwater images to a certain extent. Methods based on deep learning can be used to study the relationship between the features of an image set by training the neural network, and reduce the error caused by prior invalidity. Some characteristics of turbid water are similar to those of foggy weather, including problems regarding the attenuation of reflected light, blur caused by tiny impurities, and abnormal changes in color. These factors result in severe color distortion and low visibility [27] in the captured image, so suitable light models and algorithms need to be developed to eliminate any negative impacts. Because underwater image processing and defogging have certain similarities, various defogging algorithms have been gradually improved for application to the enhancement of underwater images. [PERSON] et al. [28] developed a fully connected convolution neural network for underwater image defogging. The integration of low-level and high-level features through the depth frame of the encoder-decoder helped to restore blurred images, showing better results than existing methods, such as the structural similarity index (SSIM) [29], peak signal to noise ratio (PSNR), and mean square error (MSE). It was also able to retain details during the removal of fog. [PERSON] et al. [30] proposed an end-to-end trainable image defogging network called LIGHT-Net, which includes a color constancy module and a haze reduction module. The color constancy module was used to remove color differences in the image caused by the weather conditions, and the haze reduction module used an initial residual module to reduce the haze effect. Feature sharing was also proposed in this module, which means the features learned at the initial level are effectively shared through the network. The experimental results of this method are promising. [PERSON] and [PERSON] [31] proposed a migration learning method for several types of naturally degraded image enhancement, including underwater image enhancement. They used transfer learning for each specific natural degradation. By repeatedly applying the general enhancement model, they overcame existing problems regarding the shortage of training datasets for in-depth learning methods and the computational burden of the training process. The enhanced model was finetuned, and its performance surpassed several of the most advanced methods designed for specific tasks, such as uwcnn [32] and funie-gan [33]. [PERSON] et al. [34] proposed a combination of image enhancement, image recovery, and the convolution neural network, resulting in a method for target detection of recovered images. Due to the maximum number of green pixels in underwater images, under dark channel prior method (UDCP)-based energy transmission restoration (UD-ETR) was proposed to process green channel images and obtain the recovered images. The image processing results are displayed in Table 3.  
On this basis, a method for fish detection in restored images using CNN was proposed, and a PC-based automatic target detection recognition visual system was developed. The training results of CNN were also shown to be significantly better than that of the traditional model, which solves the problem of inaccurate target detection caused by blurred images. [PERSON] et al. [36] proposed an effective edge perception restoration and enhancement model for severely blurred shallow coastal images with low contrast. Restoration methods, which are based on the dark channel and rolling guidance filter, were used to restore and denoise such images, resulting in clearer edge perception. This method introduced a rolling guidance filter in dark channel prior (DCP) restoration, which effectively restored images and decreased the noise in the images. This experiment showed that the rolling filter based on the recovery model has better denoising effects.