Real-time Model-based Image Color Correction for Underwater Robots

Recently, a new underwater imaging formation model presented that the coefficients related to the direct and backscatter transmission signals are dependent on the type of water, camera specifications, water depth, and imaging range. This paper proposes an underwater color correction method that integrates this new model on an underwater robot, using information from a pressure depth sensor for water depth and a visual odometry system for estimating scene distance. Experiments were performed with and without a color chart over coral reefs and a shipwreck in the Caribbean. We demonstrate the performance of our proposed method by comparing it with other statistic-, physic-, and learning-based color correction methods. Applications for our proposed method include improved 3D reconstruction and more robust underwater robot navigation.Comment: Accepted at the 2019 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS

WaterFlow: Heuristic Normalizing Flow for Underwater Image Enhancement and Beyond

Underwater images suffer from light refraction and absorption, which impairs visibility and interferes the subsequent applications. Existing underwater image enhancement methods mainly focus on image quality improvement, ignoring the effect on practice. To balance the visual quality and application, we propose a heuristic normalizing flow for detection-driven underwater image enhancement, dubbed WaterFlow. Specifically, we first develop an invertible mapping to achieve the translation between the degraded image and its clear counterpart. Considering the differentiability and interpretability, we incorporate the heuristic prior into the data-driven mapping procedure, where the ambient light and medium transmission coefficient benefit credible generation. Furthermore, we introduce a detection perception module to transmit the implicit semantic guidance into the enhancement procedure, where the enhanced images hold more detection-favorable features and are able to promote the detection performance. Extensive experiments prove the superiority of our WaterFlow, against state-of-the-art methods quantitatively and qualitatively.Comment: 10 pages, 13 figure

HybrUR: A Hybrid Physical-Neural Solution for Unsupervised Underwater Image Restoration

Robust vision restoration for an underwater image remains a challenging problem. For the lack of aligned underwater-terrestrial image pairs, the unsupervised method is more suited to this task. However, the pure data-driven unsupervised method usually has difficulty in achieving realistic color correction for lack of optical constraint. In this paper, we propose a data- and physics-driven unsupervised architecture that learns underwater vision restoration from unpaired underwater-terrestrial images. For sufficient domain transformation and detail preservation, the underwater degeneration needs to be explicitly constructed based on the optically unambiguous physics law. Thus, we employ the Jaffe-McGlamery degradation theory to design the generation models, and use neural networks to describe the process of underwater degradation. Furthermore, to overcome the problem of invalid gradient when optimizing the hybrid physical-neural model, we fully investigate the intrinsic correlation between the scene depth and the degradation factors for the backscattering estimation, to improve the restoration performance through physical constraints. Our experimental results show that the proposed method is able to perform high-quality restoration for unconstrained underwater images without any supervision. On multiple benchmarks, we outperform several state-of-the-art supervised and unsupervised approaches. We also demonstrate that our methods yield encouraging results on real-world applications

Towards High-resolution Imaging from Underwater Vehicles

Large area mapping at high resolution underwater continues to be constrained by sensor-level environmental constraints and the mismatch between available navigation and sensor accuracy. In this paper, advances are presented that exploit aspects of the sensing modality, and consistency and redundancy within local sensor measurements to build high-resolution optical and acoustic maps that are a consistent representation of the environment. This work is presented in the context of real-world data acquired using autonomous underwater vehicles (AUVs) and remotely operated vehicles (ROVs) working in diverse applications including shallow water coral reef surveys with the Seabed AUV, a forensic survey of the RMS Titanic in the North Atlantic at a depth of 4100 m using the Hercules ROV, and a survey of the TAG hydrothermal vent area in the mid-Atlantic at a depth of 3600 m using the Jason II ROV. Specifically, the focus is on the related problems of structure from motion from underwater optical imagery assuming pose instrumented calibrated cameras. General wide baseline solutions are presented for these problems based on the extension of techniques from the simultaneous localization and mapping (SLAM), photogrammetric and the computer vision communities. It is also examined how such techniques can be extended for the very different sensing modality and scale associated with multi-beam bathymetric mapping. For both the optical and acoustic mapping cases it is also shown how the consistency in mapping can be used not only for better global mapping, but also to refine navigation estimates.Peer Reviewedhttp://deepblue.lib.umich.edu/bitstream/2027.42/86051/1/hsingh-21.pd