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:  Real-world Underwater Image Datasets 
1. Abstract of Cited Paper (BIB001):  This paper describes the hardware setup of a stereovision system for underwater 3D object detection and a camera calibration method that does not require complex underwater procedures. The vision system has been developed within the MARIS Italian national project for underwater cooperative intervention tasks. The hardware architecture is based upon state of the art technologies. The software modules of the system are based on the ROS middleware. A dataset of underwater stereo images of cylindrical pipes has been collected and is made publicly available. 
2. Abstract of Cited Paper (BIB002):  Underwater vision is subject to effects of underwater light propagation that act to absorb, scatter, and attenuate light rays between the scene and the imaging platform. Backscattering has been shown to have a strong impact on underwater images. As light interacts with particulate matter in the water column, it is scattered back towards the imaging sensor, resulting in a hazy effect across the image. A similar effect occurs in terrestrial applications in images of foggy scenes due to interaction with the atmosphere. Prior work on multi-image dehazing has relied on multiple cameras, polarization filters, or moving light sources. Single image dehazing is an ill-posed problem; proposed solutions rely on strong priors of the scene. This paper presents a novel method for underwater image dehazing using a light field camera to capture both the spatial and angular distribution of light across a scene. First, a 2D dehazing method is applied to each sub-aperture image. These dehazed images are then combined to produce a smoothed central view. Lastly, the smoothed central view is used as a reference to perform guided image filtering, resulting in a 4D dehazed underwater light field image. The developed method is validated on real light field data collected in a controlled in-lab water tank, with images taken in air for reference. This dataset is made publicly available. 
3. Abstract of Cited Paper (BIB003):  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.

Literature Review:
Due to the limitations of synthetic underwater image datasets (e.g., inaccurate formation models, hard assumptions, insufficient images, specific scenes, etc.), we mainly introduce the real-world underwater image datasets in this section. • Fish4Knowledge BIB001 is funded by the European Union Seventh Framework program for the study of marine ecosystems, which provides a video and fish analysis dataset (about 200 Tb in size) 3 . • ULFID: Underwater Light Field Image Dataset BIB003 contains several underwater light field images in pure water and hazy conditions, as well as images taken in the air for reference 4 . • MARIS: Marine Autonomous Robotics for InterventionS BIB002 is to advance the development of cooperating AUVs for undersea intervention in the off- shore industry, in search-and-rescue tasks, and in various flavors of scientific exploration. This project provides several underwater images and videos captured by underwater stereo vision system 5 . • Haze-line Dataset BIB004 collected a dataset of images taken in different locations with varying water properties, showing color charts in the scenes (about 33GB in size). Moreover, the 3D structure of the scene was calculated based on stereo imaging 6 . • UIEBD: Underwater Image Enhancement Benchmark Dataset  includes 950 real-world underwater images, 890 of which have the corresponding reference images where each reference image is selected from 12 enhanced results. The rest 60 underwater images which cannot obtain satisfactory references are treated as challenging data. The UIEBD  contains a large range of image resolution and spans diverse scene/main object categories. BIB001 . The existing real-world underwater image datasets usually have monotonous content and limited quality degradation types. Moreover, these datasets did not provide the corresponding ground truth images because it is impractical to simultaneously obtain the degraded underwater image and the ground-truth of the same scene. The UIEBD  provides the corresponding reference images which can be considered for full-reference 5 http://rimlab.ce.unipr.it/Maris.html 6 http://csms.haifa.ac.il/profiles/tTreibitz/ datasets/ambient_forwardlooking/index.html 7 https://li-chongyi.github.io/proj_benchmark.html image quality assessment. We conduct experimental quantitative and visual comparisons on this dataset. Besides, to validate the generalization of current deep algorithms, we also present the visual results of different methods on another two datasets i.e., Haze-line dataset BIB004 and ULFID BIB003 . Some representative samples of these three datasets are given in Figure 3 .