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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.


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


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

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Our Approach – 2 key observations

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" The two components are (usually) easily separable in the frequency domain

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Low Pass

Filter

High Pass

Filter

Illumination field has Illumination field has lower spatial frequencies

Registration artifacts have Registration artifacts have higher spatial frequencies


Removing sunlight flicker

Input

Difference(Input (Input (Input -- Median)

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to the current frame

Predict the current illumination field

Coarsely recover the image

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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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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.


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.


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!


Single image dehazing

Original image



Corrected Corrected image



Original image


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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