Spaceborne underwater imaging

2016 IEEE International Conference on Computational Photography (ICCP)

Levin

et al. 2016

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To recover the three dimensional (3D) volumetric distribution of matter in an object, images of the object are captured from multiple directions and locations. Using these images tomographic computations extract the distribution. In highly scattering media and constrained, natural irradiance, tomography must explicitly account for off-axis scattering. Furthermore, the tomographic model and recovery must function when imaging is done in-situ, as occurs in medical imaging and ground-based atmospheric sensing. We formulate tomography that handles arbitrary orders of scattering, using a monte-carlo model. Moreover, the model is highly parallelizable in our formulation. This enables large scale rendering and recovery of volumetric scenes having a large number of variables. We solve stability and conditioning problems that stem from radiative transfer (RT) modeling in-situ.

“…(6,20) express a recursive interplay of the fields I, J, that are functions of β (see Eqs. 5,7,20). It is complex to perform recurtion per each gradient component.…”

Section: Gradient-based Optimizationmentioning

confidence: 99%

“…Recovering scenes via participating media [1][2][3][4][5][6] often focus on observing background objects [7][8][9]. However, it also important to recover the medium itself, as done in remote sensing of the atmosphere.…”

Section: Introductionmentioning

confidence: 99%

In-situ multi-view multi-scattering stochastic tomography

Holodovsky

2016 IEEE International Conference on Computational Photography (ICCP)

Levin

et al. 2016

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“…Here, β ∈ 0; ∞ is the atmospheric attenuation coefficient. The additive component a here is the airlight [21,39], given by…”

Section: B System Resolutionmentioning

confidence: 99%

Resolution loss without imaging blur

Treibitz

2012

J. Opt. Soc. Am. A

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Image recovery under noise is widely studied. However, there is little emphasis on performance as a function of object size. In this work we analyze the probability of recovery as a function of object spatial frequency. The analysis uses a physical model for the acquired signal and noise, and also accounts for potential postacquisition noise filtering. Linear-systems analysis yields an effective cutoff frequency, which is induced by noise, despite having no optical blur in the imaging model. This means that a low signal-to-noise ratio (SNR) in images causes resolution loss, similar to image blur. We further consider the effect on SNR of pointwise image formation models, such as added specular or indirect reflections, additive scattering, radiance attenuation in haze, and flash photography. 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 surface is Lambertian. The validity of a Lambertian assumption increases underwater [26,31]. We set σ RN = 13.1[e] and a max well of 24,000 [e] in a perspective camera C based on Canon 60D camera data, while L is a spot light with no lateral falloff.…”

Section: Simulationsmentioning

confidence: 99%

The Next Best Underwater View

Sheinin

2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR)

2016

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To image in high resolution large and occlusion-prone scenes, a camera must move above and around. Degradation of visibility due to geometric occlusions and distances is exacerbated by scattering, when the scene is in a participating medium. Moreover, underwater and in other media, artificial lighting is needed. Overall, data quality depends on the observed surface, medium and the timevarying poses of the camera and light source (C&L). This work proposes to optimize C&L poses as they move, so that the surface is scanned efficiently and the descattered recovery has the highest quality. The work generalizes the next best view concept of robot vision to scattering media and cooperative movable lighting. It also extends descattering to platforms that move optimally. The optimization criterion is information gain, taken from information theory. We exploit the existence of a prior rough 3D model, since underwater such a model is routinely obtained using sonar. We demonstrate this principle in a scaled-down setup.