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:  Qualitative Comparisons 
1. Abstract of Cited Paper (BIB001):  Underwater images suffer from color distortion and low contrast, because light is attenuated while it propagates through water. Attenuation under water varies with wavelength, unlike terrestrial images where attenuation is assumed to be spectrally uniform. The attenuation depends both on the water body and the 3D structure of the scene, making color restoration difficult. Unlike existing single underwater image enhancement techniques, our method takes into account multiple spectral profiles of different water types. By estimating just two additional global parameters: the attenuation ratios of the blue-red and blue-green color channels, the problem is reduced to single image dehazing, where all color channels have the same attenuation coefficients. Since the water type is unknown, we evaluate different parameters out of an existing library of water types. Each type leads to a different restored image and the best result is automatically chosen based on color distribution. We also contribute a dataset of 57 images taken in different locations. To obtain ground truth, we placed multiple color charts in the scenes and calculated its 3D structure using stereo imaging. This dataset enables a rigorous quantitative evaluation of restoration algorithms on natural images for the first time. 
2. Abstract of Cited Paper (BIB002):  In an underwater scene, wavelength-dependent light absorption and scattering degrade the visibility of images, causing low contrast and distorted color casts. To address this problem, we propose a convolutional neural network based image enhancement model, i.e., UWCNN, which is trained efficiently using a synthetic underwater image database. Unlike the existing works that require the parameters of underwater imaging model estimation or impose inflexible frameworks applicable only for specific scenes, our model directly reconstructs the clear latent underwater image by leveraging on an automatic end-to-end and data-driven training mechanism. Compliant with underwater imaging models and optical properties of underwater scenes, we first synthesize ten different marine image databases. Then, we separately train multiple UWCNN models for each underwater image formation type. Experimental results on real-world and synthetic underwater images demonstrate that the presented method generalizes well on different underwater scenes and outperforms the existing methods both qualitatively and quantitatively. Besides, we conduct an ablation study to demonstrate the effect of each component in our network. 
3. Abstract of Cited Paper (BIB003):  Underwater vision suffers from severe effects due to selective attenuation and scattering when light propagates through water. Such degradation not only affects the quality of underwater images but limits the ability of vision tasks. Different from existing image enhancement and restoration methods which either ignore the wavelength dependency of the attenuation or assume a specific spectral profile, we tackle color distortion problem of underwater image from a new view. In this letter, we propose a weakly supervised color transfer method to correct color distortion. Inspired by Cycle-Consistent Adversarial Networks, we design a multi-term loss function including adversarial loss, cycle consistency loss, and SSIM (Structural Similarity Index Measure) loss. In this way, we translate the color of underwater image as if it is taken in the air, and preserve the content and structure of original underwater image. Experiments on underwater images captured under diverse scenes show that the proposed method can produce visually pleasing results, even outperforms the art-of-the-state methods. Besides, our method can improve the performance of vision tasks. 
4. Abstract of Cited Paper (BIB004):  Improving the quality of underwater image has a significant impact on many signal processing and computer vision applications, while haze-effect and color shift are main handicaps need to be surmounted. Due to the complexity of the underwater environmental factors, most existing image enhancement techniques cannot be directly applied to address this task. In this work, we develop a novel framework to jointly performing residual learning on transmission and image domains for underwater scene entrenchment. Indeed, our deep model consists of a data-driven residual architecture for transmission estimation and a knowledge-driven scene residual formulation for underwater illumination balance. Therefore, we can aggregate the prior knowledge and data information to investigate the underlying underwater image distribution. Moreover, by introducing adaptive exposure map, image colors will also be corrected accordingly. Experimentally, both quantitative and qualitative analysis can indicate outstanding effectiveness of the proposed algorithm, against state-of-the-art approaches. 
5. Abstract of Cited Paper (BIB005):  Underwater image enhancement has received much attention in underwater vision research. However, raw underwater images easily suffer from color distortion, underexposure, and fuzz caused by the underwater scene. To address the above-mentioned problems, we propose a new multiscale dense generative adversarial network (GAN) for enhancing underwater images. The residual multiscale dense block is presented in the generator, where the multiscale, dense concatenation, and residual learning can boost the performance, render more details, and utilize previous features, respectively. And the discriminator employs computationally light spectral normalization to stabilize the training of the discriminator. Meanwhile, nonsaturating GAN loss function combining $L_1$ loss and gradient loss is presented to focus on image features of ground truth. Final enhanced results on synthetic and real underwater images demonstrate the superiority of the proposed method, which outperforms nondeep and deep learning methods in both qualitative and quantitative evaluations. Furthermore, we perform an ablation study to show the contributions of each component and carry out application tests to further demonstrate the effectiveness of the proposed method. 
6. Abstract of Cited Paper (BIB006):  Abstract Underwater image restoration, which is the keystone to the underwater vision research, is still a challenging work. The key point of underwater image restoration work is how to remove the turbidity and the color distortion caused by the underwater environment. In this paper, we propose an underwater image restoration method based on transferring an underwater style image into a recovered style using Multi-Scale Cycle Generative Adversarial Network (MCycle GAN) System. We include a Structural Similarity Index Measure loss (SSIM loss), which can provide more flexibility to model the detail structural to improve the image restoration performance. We use dark channel prior (DCP) algorithm to get the transmission map of an image and design an adaptive SSIM loss to improve underwater image quality. We input this information into the network for multi-scale calculation on the images, which achieves the combination of DCP algorithm and Cycle-Consistent Adversarial Networks (CycleGAN). By compared the quantitative and qualitative with existing state-of-the-art approaches, our method shows a pleasing performance on the underwater image dataset.

Literature Review:
We present the visual results on UIEBD  , Hazeline BIB002 and ULFID BIB001 in Figures 4-8 . The groundtruth images for Haze-line BIB002 and ULFID BIB001 are not available; hence, we furnish the visual results only for both the datasets. • Greenish tone images: In Figure 4 , we present the visual comparisons of greenish underwater images from UIEBD  for the state-of-the-art CNNbased and GAN-based methods. The GAN-based models aim to improve the perceptual quality, while CNN models are more focused on the PSNR values of the enhanced images. One can notice that the outputs of GAN methods are generally different in the tone as compared to CNN methods, as the later is more faithful to the original underwater image colors. This also contributes to the higher PSNR for the CNN methods compared to GAN methods, as shown in Table 2 . It is to be noted that in Figure 4 , we only show one of the variants in case of the same algorithm for the limited space. • Bluish tone images: Figure 5 shows the visual comparisons on two bluish images from UIEBD  consisting of a ray and statues. The bluish tone is ubiquitous in underwater images and difficult to be completely removed by current algorithms. DUIENet  and UWCNN BIB003 render the best outcomes; however, the results still have a bluish tone, especially in far distances (more severe backscatter). By contrast, the UWGAN BIB004 and DenseGAN BIB006 introduces obvious artificial colors mainly inducing by the shortcomings of their unpaired training data. • Low and high backscatter images: Backscatter is a challenging problem faced during the underwater imaginary. The leading causes of backscattering are the strobes or the internal flash, which lights up the particles in the water present between the subject and the camera lens. This phenomenon can also be observed behind the subject, lighting up the open water. With a dark background, backscattering is more natural to recognize. Here, we present two images in Figure 6 on low and high backscatter from  . The first image in Figure 6 is an example of low backscatter, while the bottom one is of high backscatter. We can visually observe that the URCNN BIB005 has over-exposed the images while the UWGAN BIB004 created some artificial colors. In addition, the low backscatter is relatively easier to be removed than the high backscatter. For the high backscatter image, none of the methods can produce visually pleasing results and current methods even introduce annoying artifacts and color casts. It should also be regarded here that UWCNN BIB003 can produce good results if the model matches the type of water. • Haze-line BIB002 images: The visual comparisons for underwater images from Haze-line dataset BIB002 is provided in Figure 7 . This dataset only provides the depth maps reconstructed from the stereo images; however, no ground-truth images are available for * , Chongyi computing the evaluation metrics. The images in this dataset are challenging since most of the images have bluish tone and high backscatter. UWGAN BIB004 and DenseGAN BIB006 provide visually promising results, but both have created false colors, and this is also the case with DUIENet  and MCycle-GAN BIB007 networks. It is obvious that all deep algorithms fall behind the performance of a conventional method BIB002 which mismatches the progress of deep learning in other low-level visual tasks. • ULFID BIB001 images: As the last example, we show the images with severe degradations from ULFID BIB001 in Figure 8 . The ground-truth images for this dataset are not feasible to evaluate the models; hence, we only present the visual results. Although the deep algorithms can remove the greenish tone from the images; however, all of them fail to furnish clear images and even amplify the noise. This dataset is an excellent example that the underwater image enhancement still requires concerted efforts to progress, and the noise in underwater images should be paid more attention in the future study.