You are presented with multiple paper abstracts, alongside the associated literature review and chapter titles. Given the provided information, you are going to do a literature review. Please generate a coherent literature review that synthesizes the content, draws connections between different works, highlights major findings, and identifies any gaps in the existing research.

Literature Review Title: Diving Deeper into Underwater Image Enhancement: A Survey 
Chapter Title:  P2P network 
1. Abstract of Cited Paper (BIB001):  In this paper, we propose a very deep fully convolutional encoding-decoding framework for image restoration such as denoising and super-resolution. The network is composed of multiple layers of convolution and de-convolution operators, learning end-to-end mappings from corrupted images to the original ones. The convolutional layers act as the feature extractor, which capture the abstraction of image contents while eliminating noises/corruptions. De-convolutional layers are then used to recover the image details. We propose to symmetrically link convolutional and de-convolutional layers with skip-layer connections, with which the training converges much faster and attains a higher-quality local optimum. First, The skip connections allow the signal to be back-propagated to bottom layers directly, and thus tackles the problem of gradient vanishing, making training deep networks easier and achieving restoration performance gains consequently. Second, these skip connections pass image details from convolutional layers to de-convolutional layers, which is beneficial in recovering the original image. Significantly, with the large capacity, we can handle different levels of noises using a single model. Experimental results show that our network achieves better performance than all previously reported state-of-the-art methods. 
2. Abstract of Cited Paper (BIB002):  Turbid underwater environment poses great difficulties for the applications of vision technologies. One of the biggest challenges is the complicated noise distribution of the underwater images due to the serious scattering and absorption. To alleviate this problem, this work proposes a deep pixel-to-pixel networks model for underwater image enhancement by designing an encoding-decoding framework. It employs the convolution layers as encoding to filter the noise, while uses deconvolution layers as decoding to recover the missing details and refine the image pixel by pixel. Moreover, skip connection is introduced in the networks model in order to avoid low-level features losing while accelerating the training process. The model achieves the image enhancement in a self-adaptive data-driven way rather than considering the physical environment. Several comparison experiments are carried out with different datasets. Results show that it outperforms the state-of-the-art image restoration methods in underwater image defogging, denoising and colour enhancement.

Literature Review:
Recently, Sun et al. BIB003 suggested the use of pixelto-pixel (P2P) network to enhance underwater images. The proposed model is a "symmetric" encoder and decoder network similar to REDNet BIB002 . The encoder part is composed of three convolutional layers, while the decoder is made from three deconvolutional layers. ReLU follows each network element except the last one. This model is trained on 3359 images collected from the real-world environment. To simulate the underwater images, the authors pour milk of 30, 50, and 70 ml into 1m 3 of water to produce low, medium and high-level degradation, respectively. Finally, out of these, 10,000 images are selected for training and another 2,000 images for testing. Moreover, the input to the network is a cropped patch of 66×66. The loss function is 2 minimized via SGD BIB001 with an initial learning rate of 10 −7 .