This paper proposes a methodology to estimate the transmission in underwater environments which consists on an adaptation of the Dark Channel Prior (DCP), a statistical prior based on properties of images obtained in outdoor natural scenes. Our methodology, called Underwater DCP (UDCP), basically considers that the blue and green color channels are the underwater visual information source, which enables a significant improvement over existing methods based in DCP. This is shown through a comparative study with state of the art techniques, we present a detailed analysis of our technique which shows its applicability and limitations in images acquired from real and simulated scenes.

In underwater environments, the scattering and absorption phenomena affect the propagation of light, degrading the quality of captured images. In this work, the authors present a method based on a physical model of light propagation that takes into account the most significant effects to image degradation: absorption, scattering, and backscattering. The proposed method uses statistical priors to restore the visual quality of the images acquired in typical underwater scenarios.

We present and discuss a distributed scheme for multi-robot cooperation. It integrates mission planning and task refinement as well as cooperative mechanisms adapted from the contract net protocol framework. We discuss its role and how it can be integrated as a component of a complete robot control system. We also discuss how it handles distributed task allocation and achievement as well as cooperative reaction to contingencies. Finally, we illustrate its use through a simulated system, which allows a number of robots to perform load transfer tasks in a route network environment.

Image restoration methods have been made to repair images that have some kind of degradation. Most of these methods are designed to deal with the degradation caused by the over-land effects. However, when the images was captured in underwater environments, there are different properties that can degrade the image in unusual ways. This work aims to evaluate how the popular image restoration methods behaves when applied in underwater images with the presence of turbidity in the water. For this, we propose a dataset where we are able to control the amount of image degradation due to underwater properties on a scenario with 3D objects that represents the seabed characteristics. After that, we evaluate the restoration of these methods and their behavior through the image degradation due to turbidity.