59 research outputs found
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
Theoretical optimal modulation frequencies for scattering parameter estimation and ballistic photon filtering in diffusive media
The efficiency of using intensity modulated light for estimation of
scattering properties of a turbid medium and for ballistic photon
discrimination is theoretically quantified in this article. Using the diffusion
model for modulated photon transport and considering a noisy quadrature
demodulation scheme, the minimum-variance bounds on estimation of parameters of
interest are analytically derived and analyzed. The existence of a
variance-minimizing optimal modulation frequency is shown and its evolution
with the properties of the intervening medium is derived and studied.
Furthermore, a metric is defined to quantify the efficiency of ballistic photon
filtering which may be sought when imaging through turbid media. The analytical
derivation of this metric shows that the minimum modulation frequency required
to attain significant ballistic discrimination depends only on the reduced
scattering coefficient of the medium in a linear fashion for a highly
scattering medium
Underwater reconstruction using depth sensors
In this paper we describe experiments in which we acquire range images of underwater surfaces with four types of depth sensors and attempt to reconstruct underwater surfaces. Two conditions are tested: acquiring range images by submersing the sensors and by holding the sensors over the water line and recording through water. We found out that only the Kinect sensor is able to acquire depth images of submersed surfaces by holding the sensor above water. We compare the reconstructed underwater geometry with meshes obtained when the surfaces were not submersed. These findings show that 3D underwater reconstruction using depth sensors is possible, despite the high water absorption of the near infrared spectrum in which these sensors operate
Underwater Image Enhancement Using An Integrated Colour Model.
In underwater situations, clarity of images are degraded by light absorption and scattering. This causes one colour to dominate the image. In order to improve the perception of underwater images, we proposed an approach based on slide stretching
Cast-Gan: Learning to Remove Colour Cast from Underwater Images
Underwater images are degraded by blur and colour cast caused by the attenuation of light in water. To remove the colour cast with neural networks, images of the scene taken under white illumination are needed as reference for training, but are generally unavailable. As an alternative, one can use surrogate reference images taken close to the water surface or degraded images synthesised from reference datasets. However, the former still suffer from colour cast and the latter generally have limited colour diversity. To address these problems, we exploit open data and typical colour distributions of objects to create a synthetic image dataset that reflects degradations naturally occurring in underwater photography. We use this dataset to train Cast-GAN, a Generative Adversarial Network whose loss function includes terms that eliminate artefacts that are typical of underwater images enhanced with neural networks. We compare the enhancement results of Cast-GAN with four state-of-the-art methods and validate the cast removal with a subjective evaluation
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