Fabbri, Cameron2018-11-282018-11-282018-08https://hdl.handle.net/11299/201008University of Minnesota M.S. thesis. August 2018. Major: Computer Science. Advisor: Junaed Sattar. 1 computer file (PDF); vii, 97 pages.Autonomous robots rely on a variety of sensors – acoustic, inertial, and visual – for intelligent decision making. Due to its non-intrusive, passive nature, and high information content, vision is an attractive sensing modality. However, many environments contain natural sources of visual noise such as snow, rain, dust, and other forms of distortion. This work focuses on the underwater environment, in which visual noise is a prominent component. Factors such as light refraction and absorption, suspended particles in the water, and color distortion affect the quality of visual data, resulting in noisy and distorted images. Autonomous Underwater Vehicles (AUVs) that rely on visual sensing thus face difficult challenges, and consequently exhibit poor performance on vision driven tasks. This thesis proposes a method to improve the quality of visual underwater scenes using Generative Adversarial Networks (GANs), with the goal of improving input to vision-driven behaviors further down the autonomy pipeline. Furthermore, we show how recently proposed methods are able to generate a dataset for the purpose of such underwater image restoration. For any visually-guided underwater robots, this improvement can result in increased safety and reliability through robust visual perception. To that effect, we present quantitative and qualitative data which demonstrates that images corrected through the proposed approach generate more visually appealing images, and also provide increased accuracy for a diver tracking algorithm.enColor CorrectionDeep LearningGenerative Adversarial NetworksMachine LearningRoboticsUnderwater VisionThesis or Dissertation