Self-Supervised Monocular Depth Underwater

Depth estimation is critical for any robotic system. In the past years estimation of depth from monocular images have shown great improvement, however, in the underwater environment results are still lagging behind due to appearance changes caused by the medium. So far little effort has been invested on overcoming this. Moreover, underwater, there are more limitations for using high resolution depth sensors, this makes generating ground truth for learning methods another enormous obstacle. So far unsupervised methods that tried to solve this have achieved very limited success as they relied on domain transfer from dataset in air. We suggest training using subsequent frames self-supervised by a reprojection loss, as was demonstrated successfully above water. We suggest several additions to the self-supervised framework to cope with the underwater environment and achieve state-of-the-art results on a challenging forward-looking underwater dataset

Use of commercial off-the-shelf digital cameras for scientific data acquisition and scene-specific color calibration

Author Posting. © Optical Society of America, 2014. This article is posted here by permission of Optical Society of America for personal use, not for redistribution. The definitive version was published in Journal of the Optical Society of America A: Optics, Image Science, and Vision 31 (2014): 312-321, doi:10.1364/JOSAA.31.000312.Commercial off-the-shelf digital cameras are inexpensive and easy-to-use instruments that can be used for quantitative scientific data acquisition if images are captured in raw format and processed so that they maintain a linear relationship with scene radiance. Here we describe the image-processing steps required for consistent data acquisition with color cameras. In addition, we present a method for scene-specific color calibration that increases the accuracy of color capture when a scene contains colors that are not well represented in the gamut of a standard color-calibration target. We demonstrate applications of the proposed methodology in the fields of biomedical engineering, artwork photography, perception science, marine biology, and underwater imaging.T. Treibitz is an Awardee of the Weizmann Institute of Science—National Postdoctoral Award Program for Advancing Women in Science and was supported by NSF grant ATM-0941760. D. Akkaynak, J. Allen, and R. Hanlon were supported by NSF grant 1129897 and ONR grants N0001406-1-0202 and N00014-10-1-0989 and U. Demirci by grants R01AI093282, R01AI081534, and NIH U54EB15408. J. Allen is grateful for support from a National Defense Science and Engineering Graduate Fellowship

Spectral Diversity and Regulation of Coral Fluorescence in a Mesophotic Reef Habitat in the Red Sea

The phenomenon of coral fluorescence in mesophotic reefs, although well described for shallow waters, remains largely unstudied. We found that representatives of many scleractinian species are brightly fluorescent at depths of 50–60 m at the Interuniversity Institute for Marine Sciences (IUI) reef in Eilat, Israel. Some of these fluorescent species have distribution maxima at mesophotic depths (40–100 m). Several individuals from these depths displayed yellow or orange-red fluorescence, the latter being essentially absent in corals from the shallowest parts of this reef. We demonstrate experimentally that in some cases the production of fluorescent pigments is independent of the exposure to light; while in others, the fluorescence signature is altered or lost when the animals are kept in darkness. Furthermore, we show that green-to-red photoconversion of fluorescent pigments mediated by short-wavelength light can occur also at depths where ultraviolet wavelengths are absent from the underwater light field. Intraspecific colour polymorphisms regarding the colour of the tissue fluorescence, common among shallow water corals, were also observed for mesophotic species. Our results suggest that fluorescent pigments in mesophotic reefs fulfil a distinct biological function and offer promising application potential for coral-reef monitoring and biomedical imaging

Needs and gaps in optical underwater technologies and methods for the investigation of marine animal forest 3D-structural complexity

Marine animal forests are benthic communities dominated by sessile suspension feeders (such as sponges, corals, and bivalves) able to generate three-dimensional (3D) frameworks with high structural complexity. The biodiversity and functioning of marine animal forests are strictly related to their 3D complexity. The present paper aims at providing new perspectives in underwater optical surveys. Starting from the current gaps in data collection and analysis that critically limit the study and conservation of marine animal forests, we discuss the main technological and methodological needs for the investigation of their 3D structural complexity at different spatial and temporal scales. Despite recent technological advances, it seems that several issues in data acquisition and processing need to be solved, to properly map the different benthic habitats in which marine animal forests are present, their health status and to measure structural complexity. Proper precision and accuracy should be chosen and assured in relation to the biological and ecological processes investigated. Besides, standardized methods and protocols are strictly necessary to meet the FAIR (findability, accessibility, interoperability, and reusability) data principles for the stewardship of habitat mapping and biodiversity, biomass, and growth data

Recovery Limits in Pointwise Degradation

Pointwise image formation models appear in a variety of computational vision and photography problems. Prior studies aim to recover visibility or reflectance under the effects of specular or indirect reflections, additive scattering, radiance attenuation in haze and flash, etc. This work considers bounds to recovery from pointwise degradation. The analysis uses a physical model for the acquired signal and noise, and also accounts for potential post-acquisition noise filtering. Linear-systems analysis yields an effective cutofffrequency, which is induced by noise, despite having no optical blur in the imaging model. We apply this analysis to hazy images. The result is a tool that assesses the ability to recover (within a desirable success rate) an object or feature having a certain size, distance from the camera, and radiance difference from its nearby background, per attenuation coefficient of the medium. The bounds rely on the camera specifications. The theory considers the pointwise degradation that exists in the scene during acquisition, which fundamentally limits recovery, even if the parameters of an algorithm are perfectly set. 1

Flat refractive geometry

While the study of geometry has mainly concentrated on single-viewpoint (SVP) cameras, there is growing attention to more general non-SVP systems. Here we study an important class of systems that inherently have a non-SVP: a perspective camera imaging through an interface into a medium. Such systems are ubiquitous: they are common when looking into water-based environments. The paper analyzes the common flat-interface class of systems. It characterizes the locus of the viewpoints (caustic) of this class, and proves that the SVP model is invalid in it. This may explain geometrical errors encountered in prior studies. Our physics-based model is parameterized by the distance of the lens from the medium interface, beside the focal length. The physical parameters are calibrated by a simple approach that can be based on a single-frame. This directly determines the system geometry. The calibration is then used to compensate for modeled system distortion. Based on this model, geometrical measurements of objects are significantly more accurate, than if based on an SVP model. This is demonstrated in real-world experiments. 1