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We need robots that hover…
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Optical mapping
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Most of the features that you detect (that show up) in the Most of the features that you detect (that show up) in the underwater environment are kind of non
Most of the features that you detect (that show up) in the underwater environment are kind of nonunderwater environment are kind of non-
Most of the features that you detect (that show up) in the Most of the features that you detect (that show up) in the Most of the features that you detect (that show up) in the underwater environment are kind of nonunderwater environment are kind of non--discriminant
Most of the features that you detect (that show up) in the discriminantdiscriminant.
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Removing sunlight flicker
Refracted sunlight creates irradiance (light) fluctuations
Refracted SunlightRefracted Sunlight•
Refracted SunlightRefracted SunlightCan disrupt image processing algorithms (matching and segmentation)
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Can disrupt image processing algorithms (matching and segmentation)Makes it harder to interpret benthic structures
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Our Approach – 2 key observations
! Observation 1
" The difference between an image and the temporal median has two components
Component 1 – Instant illumination field from sun light
Component 2 – Artifacts from registration errors
Original image Temporal Median
- =
Difference
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Our Approach – 2 key observations
! Observation 2
" The two components are (usually) easily separable in the frequency domain
Difference
Low Pass
Filter
High Pass
Filter
Illumination field has Illumination field has lower spatial frequencies
Registration artifacts have Registration artifacts have higher spatial frequencies
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Removing sunlight flicker
Input
Difference(Input (Input (Input -- Median)
Output
Temporal Median Illumination field(low pass difference)
Residue (Output (Output (Output -- Median)
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Warp previous illumination field
to the current frame
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Finding homographybetween the previously
recovered image and current
coarsely recovered image
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A. Shihavuddin, N. Gracias, R. Garcia. "Online Sunflicker Removal using Dynamic Texture Prediction". International Conference on Computer Vision Theory and Applications, pp. 161-167, Rome (Italy).
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L. Neumann, R. Garcia, J. Basa, R. Hegedus. "Acquisition and Visualization Techniques for Narrow Spectral
Color Imaging", Journal of the Optical Society of America A. Vol. 30, no. 6, pp. 1039–1052, 2013.
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Dark channel prior (He et al.):
Most Most local patches in hazelocal patches in haze-local patches in haze-free outdoor images
contain contain some pixels which have very low intensities in
at least one color channel, so that for these pixels:
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Instead of depthInstead of depth-Instead of depth-based fitting, first let’s have the estimation of ‘Veiling light’ based fitting, first let’s have the estimation of ‘Veiling light’ in
the blue channel by the blue channel by TarelTarel-Tarel-HautiHautière method.
Then Then seek darkest points in red and green, and seek darkest points in red and green, and fit the blue veiling light map ((#
Then Then seek darkest points in red and green, and seek darkest points in red and green, and #### optical depth map) to this points
seek darkest points in red and green, and seek darkest points in red and green, and fit the blue veiling light map fit the blue veiling light map seek darkest points in red and green, and optical depth map) to this points optical depth map) to this points with keeping its gradient structure
fit the blue veiling light map fit the blue veiling light map with keeping its gradient structurewith keeping its gradient structure. In this way we can with keeping its gradient structurewith keeping its gradient structure. In this way we can . In this way we can
expectedly strip the haze completely.
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Instead of depthInstead of depth-Instead of depth-based fitting, first let’s have the estimation of ‘Veiling light’ in the blue based fitting, first let’s have the estimation of ‘Veiling light’ in the blue channel by
based fitting, first let’s have the estimation of ‘Veiling light’ in the blue based fitting, first let’s have the estimation of ‘Veiling light’ in the blue channel by channel by Tarel
based fitting, first let’s have the estimation of ‘Veiling light’ in the blue TarelTarel-
based fitting, first let’s have the estimation of ‘Veiling light’ in the blue based fitting, first let’s have the estimation of ‘Veiling light’ in the blue based fitting, first let’s have the estimation of ‘Veiling light’ in the blue TarelTarel-Hauti
based fitting, first let’s have the estimation of ‘Veiling light’ in the blue based fitting, first let’s have the estimation of ‘Veiling light’ in the blue HautiHautière method.
Then Then seek darkest points in red and green, and seek darkest points in red and green, and fit the blue veiling light map fit the blue veiling light map ((#Then Then seek darkest points in red and green, and seek darkest points in red and green, and seek darkest points in red and green, and fit the blue veiling light map fit the blue veiling light map fit the blue veiling light map (####optical depth map) to this points with keeping its gradient structure. In this way we can optical depth map) to this points with keeping its gradient structure. In this way we can optical depth map) to this points with keeping its gradient structure. In this way we can
expectedly strip the haze completely.
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Instead of depthInstead of depth-Instead of depth-based fitting, first let’s have the estimation of ‘Veiling light’ in the blue based fitting, first let’s have the estimation of ‘Veiling light’ in the blue channel by
based fitting, first let’s have the estimation of ‘Veiling light’ in the blue based fitting, first let’s have the estimation of ‘Veiling light’ in the blue channel by channel by Tarel
based fitting, first let’s have the estimation of ‘Veiling light’ in the blue TarelTarel-
based fitting, first let’s have the estimation of ‘Veiling light’ in the blue based fitting, first let’s have the estimation of ‘Veiling light’ in the blue based fitting, first let’s have the estimation of ‘Veiling light’ in the blue TarelTarel-Hauti
based fitting, first let’s have the estimation of ‘Veiling light’ in the blue based fitting, first let’s have the estimation of ‘Veiling light’ in the blue HautiHautière method
based fitting, first let’s have the estimation of ‘Veiling light’ in the blue ère methodère method.
Then Then seek darkest points in red and green, and fit the blue veiling light map seek darkest points in red and green, and fit the blue veiling light map ((## optical depth Then Then seek darkest points in red and green, and fit the blue veiling light map seek darkest points in red and green, and fit the blue veiling light map seek darkest points in red and green, and fit the blue veiling light map (#### optical depth optical depth map) to this points with keeping its gradient structure. In this way we can expectedly strip
optical depth optical depth map) to this points with keeping its gradient structure. In this way we can expectedly strip map) to this points with keeping its gradient structure. In this way we can expectedly strip the map) to this points with keeping its gradient structure. In this way we can expectedly strip
haze map) to this points with keeping its gradient structure. In this way we can expectedly strip map) to this points with keeping its gradient structure. In this way we can expectedly strip
haze haze completelymap) to this points with keeping its gradient structure. In this way we can expectedly strip map) to this points with keeping its gradient structure. In this way we can expectedly strip the the map) to this points with keeping its gradient structure. In this way we can expectedly strip
completelycompletely. BLUE CHANNEL CORRECTED SHAPE!
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Single image dehazing
Original image
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Single image dehazing
Corrected Corrected image
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Single image dehazing
Original image
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Single image Single image dehazingdehazing –– possible artifacts (color, halo, etc.)
AllAll darkdark channelchannel methodmethod has has somesome problemproblem (((artifactsartifacts, , speedspeed)
AArtifacts, based on widely used dark channel method,
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