Underwater Image Processing: State of the Art of Restoration and Image Enhancement Methods

The underwater image processing area has received considerable attention within the last decades, showing important achievements. In this paper we review some of the most recent methods that have been specifically developed for the underwater environment. These techniques are capable of extending the range of underwater imaging, improving image contrast and resolution. After considering the basic physics of the light propagation in the water medium, we focus on the different algorithms available in the literature. The conditions for which each of them have been originally developed are highlighted as well as the quality assessment methods used to evaluate their performance

Enhancement of Underwater Video Mosaics for Post-Processing

Mosaics of seafloor created from still images or video acquired underwater have proved to be useful for construction of maps of forensic and archeological sites, species\u27 abundance estimates, habitat characterization, etc. Images taken by a camera mounted on a stable platform are registered (at first pair-wise and then globally) and assembled in a high resolution visual map of the surveyed area. While this map is usually sufficient for a human orientation and even quantitative measurements, it often contains artifacts that complicate an automatic post-processing (for example, extraction of shapes for organism counting, or segmentation for habitat characterization). The most prominent artifacts are inter-frame seams caused by inhomogeneous artificial illumination, and local feature misalignments due to parallax effects - result of an attempt to represent a 3D world on a 2D map. In this paper we propose two image processing techniques for mosaic quality enhancement - median mosaic-based illumination correction suppressing appearance of inter-frame seams, and micro warping decreasing influence of parallax effects

Generation and processing of simulated underwater images for infrastructure visual inspection with UUVs

The development of computer vision algorithms for navigation or object detection is one of the key issues of underwater robotics. However, extracting features from underwater images is challenging due to the presence of lighting defects, which need to be counteracted. This requires good environmental knowledge, either as a dataset or as a physic model. The lack of available data, and the high variability of the conditions, makes difficult the development of robust enhancement algorithms. A framework for the development of underwater computer vision algorithms is presented, consisting of a method for underwater imaging simulation, and an image enhancement algorithm, both integrated in the open-source robotics simulator UUV Simulator. The imaging simulation is based on a novel combination of the scattering model and style transfer techniques. The use of style transfer allows a realistic simulation of different environments without any prior knowledge of them. Moreover, an enhancement algorithm that successfully performs a correction of the imaging defects in any given scenario for either the real or synthetic images has been developed. The proposed approach showcases then a novel framework for the development of underwater computer vision algorithms for SLAM, navigation, or object detection in UUV

Cast-Gan: Learning to Remove Colour Cast from Underwater Images

Underwater images are degraded by blur and colour cast caused by the attenuation of light in water. To remove the colour cast with neural networks, images of the scene taken under white illumination are needed as reference for training, but are generally unavailable. As an alternative, one can use surrogate reference images taken close to the water surface or degraded images synthesised from reference datasets. However, the former still suffer from colour cast and the latter generally have limited colour diversity. To address these problems, we exploit open data and typical colour distributions of objects to create a synthetic image dataset that reflects degradations naturally occurring in underwater photography. We use this dataset to train Cast-GAN, a Generative Adversarial Network whose loss function includes terms that eliminate artefacts that are typical of underwater images enhanced with neural networks. We compare the enhancement results of Cast-GAN with four state-of-the-art methods and validate the cast removal with a subjective evaluation

3D Information from Scattering Media Images

Scattering media environments are real-world conditions that occur often, in daily life. Some examples of scattering media are haze, fog, and other bad weather conditions. In these environments, micro-particles in the surrounding media interfere with light propagation and image formation. Thus, images that are captured in these scattering media environments will suffer from low contrast and loss of intensity. This becomes an issue for computer vision methods that employ features found in the scene. To solve this issue, many approaches must estimate the corresponding clear scene prior to further processing. However, the image formation model in scattering media shows potential 3D distance information about the scene encoded implicitly in image intensities. In this paper, we investigate the potential information that can be extracted directly from the scattering media images. We demonstrate the possibility of extracting relative depth in the form of transmission as well as explicit depth maps from single images

Visually pleasant blending techniques in underwater mosaicing

of two or more images that are then combined into a single and usually larger one. The applications of mosaicing comprehend panoramic photography, super-resolution, virtual environments and vision based navigation systems, as a most relevant exponents. Besides generic camera issues as geometric and chromatic distortions, underwa-ter images are aff ected by particular factors as non-uniform illumination, caustics, blurring, suspended particles and scattering, making even more diffi cult the alignment and blend-ing. The aim of this work is to perform a re-view on the existing image blending techniques specially focusing the study on its application on the underwater imaging