Advanced Underwater Image Restoration in Complex Illumination Conditions

Underwater image restoration has been a challenging problem for decades since the advent of underwater photography. Most solutions focus on shallow water scenarios, where the scene is uniformly illuminated by the sunlight. However, the vast majority of uncharted underwater terrain is located beyond 200 meters depth where natural light is scarce and artificial illumination is needed. In such cases, light sources co-moving with the camera, dynamically change the scene appearance, which make shallow water restoration methods inadequate. In particular for multi-light source systems (composed of dozens of LEDs nowadays), calibrating each light is time-consuming, error-prone and tedious, and we observe that only the integrated illumination within the viewing volume of the camera is critical, rather than the individual light sources. The key idea of this paper is therefore to exploit the appearance changes of objects or the seafloor, when traversing the viewing frustum of the camera. Through new constraints assuming Lambertian surfaces, corresponding image pixels constrain the light field in front of the camera, and for each voxel a signal factor and a backscatter value are stored in a volumetric grid that can be used for very efficient image restoration of camera-light platforms, which facilitates consistently texturing large 3D models and maps that would otherwise be dominated by lighting and medium artifacts. To validate the effectiveness of our approach, we conducted extensive experiments on simulated and real-world datasets. The results of these experiments demonstrate the robustness of our approach in restoring the true albedo of objects, while mitigating the influence of lighting and medium effects. Furthermore, we demonstrate our approach can be readily extended to other scenarios, including in-air imaging with artificial illumination or other similar cases

A Calibration Tool for Refractive Underwater Vision

Many underwater robotic applications relying on vision sensors require proper camera calibration, i.e. knowing the incoming light ray for each pixel in the image. While for the ideal pinhole camera model all viewing rays intersect in a single 3D point, underwater cameras suffer from - possibly multiple - refractions of light rays at the interfaces of water, glass and air. These changes of direction depend on the position and orientation of the camera inside the water-proof housing, as well as on the shape and properties of the optical window, the port, itself. In recent years explicit models for underwater vision behind common ports such as flat or dome port have been proposed, but the underwater community is still lacking a calibration tool which can determine port parameters through refractive calibration. With this work we provide the first open source implementation of an underwater refractive camera calibration toolbox. It allows end-to-end calibration of underwater vision systems, including camera, stereo and housing calibration for systems with dome or flat ports. The implementation is verified using rendered datasets and real-world experiments.Comment: 7 pages, 5 figures, the paper is submitted to the 2024 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS 2024

Refractive Geometry for Underwater Domes

Underwater cameras are typically placed behind glass windows to protect them from the water. Spherical glass, a dome port, is well suited for high water pressures at great depth, allows for a large field of view, and avoids refraction if a pinhole camera is positioned exactly at the sphere’s center. Adjusting a real lens perfectly to the dome center is a challenging task, both in terms of how to actually guide the centering process (e.g. visual servoing) and how to measure the alignment quality, but also, how to mechanically perform the alignment. Consequently, such systems are prone to being decentered by some offset, leading to challenging refraction patterns at the sphere that invalidate the pinhole camera model. We show that the overall camera system becomes an axial camera, even for thick domes as used for deep sea exploration and provide a non-iterative way to compute the center of refraction without requiring knowledge of exact air, glass or water properties. We also analyze the refractive geometry at the sphere, looking at effects such as forward- vs. backward decentering, iso-refraction curves and obtain a 6th-degree polynomial equation for forward projection of 3D points in thin domes. We then propose a pure underwater calibration procedure to estimate the decentering from multiple images. This estimate can either be used during adjustment to guide the mechanical position of the lens, or can be considered in photogrammetric underwater applications

Refractive Geometry for Underwater Domes

Underwater cameras are typically placed behind glass windows to protect them from the water. Spherical glass, a dome port, is well suited for high water pressures at great depth, allows for a large field of view, and avoids refraction if a pinhole camera is positioned exactly at the sphere’s center. Adjusting a real lens perfectly to the dome center is a challenging task, both in terms of how to actually guide the centering process (e.g. visual servoing) and how to measure the alignment quality, but also, how to mechanically perform the alignment. Consequently, such systems are prone to being decentered by some offset, leading to challenging refraction patterns at the sphere that invalidate the pinhole camera model. We show that the overall camera system becomes an axial camera, even for thick domes as used for deep sea exploration and provide a non-iterative way to compute the center of refraction without requiring knowledge of exact air, glass or water properties. We also analyze the refractive geometry at the sphere, looking at effects such as forward- vs. backward decentering, iso-refraction curves and obtain a 6th-degree polynomial equation for forward projection of 3D points in thin domes. We then propose a pure underwater calibration procedure to estimate the decentering from multiple images. This estimate can either be used during adjustment to guide the mechanical position of the lens, or can be considered in photogrammetric underwater applications

Deep Sea Robotic Imaging Simulator

Nowadays underwater vision systems are being widely applied in ocean research. However, the largest portion of the ocean - the deep sea - still remains mostly unexplored. Only relatively few image sets have been taken from the deep sea due to the physical limitations caused by technical challenges and enormous costs. Deep sea images are very different from the images taken in shallow waters and this area did not get much attention from the community. The shortage of deep sea images and the corresponding ground truth data for evaluation and training is becoming a bottleneck for the development of underwater computer vision methods. Thus, this paper presents a physical model-based image simulation solution, which uses an in-air texture and depth information as inputs, to generate underwater image sequences taken by robots in deep ocean scenarios. Different from shallow water conditions, artificial illumination plays a vital role in deep sea image formation as it strongly affects the scene appearance. Our radiometric image formation model considers both attenuation and scattering effects with co-moving spotlights in the dark. By detailed analysis and evaluation of the underwater image formation model, we propose a 3D lookup table structure in combination with a novel rendering strategy to improve simulation performance. This enables us to integrate an interactive deep sea robotic vision simulation in the Unmanned Underwater Vehicles simulator. To inspire further deep sea vision research by the community, we release the source code of our deep sea image converter to the public (https://www.geomar.de/en/omv-research/robotic-imaging-simulator)

Virtually throwing benchmarks into the ocean for deep sea photogrammetry and image processing evaluation

Vision in the deep sea is acquiring increasing interest from many fields as the deep seafloor represents the largest surface portion onEarth. Unlike common shallow underwater imaging, deep sea imaging requires artificial lighting to illuminate the scene in perpetualdarkness. Deep sea images suffer from degradation caused by scattering, attenuation and effects of artificial light sources and havea very different appearance to images in shallow water or on land. This impairs transferring current vision methods to deep seaapplications. Development of adequate algorithms requires some data with ground truth in order to evaluate the methods. However,it is practically impossible to capture a deep sea scene also without water or artificial lighting effects. This situation impairs progressin deep sea vision research, where already synthesized images with ground truth could be a good solution. Most current methodseither render a virtual 3D model, or use atmospheric image formation models to convert real world scenes to appear as in shallowwater appearance illuminated by sunlight. Currently, there is a lack of image datasets dedicated to deep sea vision evaluation. Thispaper introduces a pipeline to synthesize deep sea images using existing real world RGB-D benchmarks, and exemplarily generatesthe deep sea twin datasets for the well known Middlebury stereo benchmarks. They can be used both for testing underwater stereomatching methods and for training and evaluating underwater image processing algorithms. This work aims towards establishingan image benchmark, which is intended particularly for deep sea vision developments

Optical Imaging and Image Restoration Techniques for Deep Ocean Mapping: A Comprehensive Survey

Visual systems are receiving increasing attention in underwater applications. While the photogrammetric and computer vision literature so far has largely targeted shallow water applications, recently also deep sea mapping research has come into focus. The majority of the seafloor, and of Earth’s surface, is located in the deep ocean below 200 m depth, and is still largely uncharted. Here, on top of general image quality degradation caused by water absorption and scattering, additional artificial illumination of the survey areas is mandatory that otherwise reside in permanent darkness as no sunlight reaches so deep. This creates unintended non-uniform lighting patterns in the images and non-isotropic scattering effects close to the camera. If not compensated properly, such effects dominate seafloor mosaics and can obscure the actual seafloor structures. Moreover, cameras must be protected from the high water pressure, e.g. by housings with thick glass ports, which can lead to refractive distortions in images. Additionally, no satellite navigation is available to support localization. All these issues render deep sea visual mapping a challenging task and most of the developed methods and strategies cannot be directly transferred to the seafloor in several kilometers depth. In this survey we provide a state of the art review of deep ocean mapping, starting from existing systems and challenges, discussing shallow and deep water models and corresponding solutions. Finally, we identify open issues for future lines of research

An Optical Digital Twin for Underwater Photogrammetry: GEODT - A Geometrically Verified Optical Digital Twin for Development, Evaluation, Training, Testing and Tuning of Multi-Media Refractive Algorithms

Most parts of the Earth’s surface are situated in the deep ocean. To explore this visually rather adversarial environment with cameras, they have to be protected by pressure housings. These housings, in turn, need interfaces to the world, enduring extreme pressures within the water column. Commonly, a flat window or a half-sphere of glass, called flat-port or dome-port, respectively is used to implement such kind of interface. Hence, multi-media interfaces, between water, glass and air are introduced, entailing refraction effects in the images taken through them. To obtain unbiased 3D measurements and to yield a geometrically faithful reconstruction of the scene, it is mandatory to deal with the effects in a proper manner. Hence, we propose an optical digital twin of an underwater environment, which has been geometrically verified to resemble a real water lab tank that features the two most common optical interfaces. It can be used to develop, evaluate, train, test and tune refractive algorithms. Alongside this paper, we publish the model for further extension, jointly with code to dynamically generate samples from the dataset. Finally, we also publish a pre-rendered dataset ready for use at https://git.geomar.de/david-nakath/geodt

Puncta-localized TRAF domain protein TC1b contributes to the autoimmunity of snc1

Immune receptors play important roles in the perception of pathogens and initiation of immune responses in both plants and animals. Intracellular nucleotide-binding domain leucine-rich repeat (NLR)-type receptors constitute a major class of receptors in vascular plants. In the Arabidopsis thaliana mutant suppressor of npr1-1, constitutive 1 (snc1), a gain-of-function mutation in the NLR gene SNC1 leads to SNC1 overaccumulation and constitutive activation of defense responses. From a CRISPR/Cas9-based reverse genetics screen in the snc1 autoimmune background, we identified that mutations in TRAF CANDIDATE 1b (TC1b), a gene encoding a protein with four tumor necrosis factor receptor-associated factor (TRAF) domains, can suppress snc1 phenotypes. TC1b does not appear to be a general immune regulator as it is not required for defense mediated by other tested immune receptors. TC1b also does not physically associate with SNC1, affect SNC1 accumulation, or affect signaling of the downstream helper NLRs represented by ACTIVATED DISEASE RESISTANCE PROTEIN 1-L2 (ADR1-L2), suggesting that TC1b impacts snc1 autoimmunity in a unique way. TC1b can form oligomers and localizes to punctate structures of unknown function. The puncta localization of TC1b strictly requires its coiled-coil (CC) domain, whereas the functionality of TC1b requires the four TRAF domains in addition to the CC. Overall, we uncovered the TRAF domain protein TC1b as a novel positive contributor to plant immunity