Blind Contrast Restoration Assessment by Gradient Ratioing at Visible Edges

1
Blind Contrast Restoration Assessment by Gradient
Ratioing at Visible Edges

• Nicolas Hautière1, Jean-Philippe Tarel1, Didier
  Aubert1-2, Eric Dumont1

1Laboratoire Central des Ponts et Chaussées,
Paris, France 2Institut National de REcherche sur
les Transports et leur Sécurité, Versailles,
France
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Presentation Overview

1. Problematic
2. Visibility Model
3. Visible Edges Ratioing
4. Visual Properties of Fog
5. Contrast Restoration
6. Visible Edges Segmentation
7. Contrast Restoration Assessment
8. Conclusion

3
Problematic

• There is a lack of methodology to assess the
  performances of fog degraded images restoration.
• Since fog effects are volumetric, fog can not be
  considered as a classical image noise or
  degradation which might be added and then
  removed.
• Consequently, compared to image quality
  assessment or image restoration areas, there is
  no easy way, synthetic images from 3D models put
  aside, to have a reference image.
• We propose such a contribution.

4
Visibility Model

• Visibility can be related to the contrast C,
  defined by
• For suprathreshold contrasts, the Visibility
  Level (VL) of a target can be quantified by the
  ratio

• As Lb is the same for both conditions, then this
  equation reduces to
• ?Lthreshold depends on many parameters and can be
  estimated using Adrians empirical target
  visibility model (Adrian, 1989).

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Visible Edges Ratioing

• To assess the performances of a contrast
  restoration method, we compute, for each pixel
  belonging to a visible edge in the restored
  image, the ratio
• ?Io is the gradient in the original image.
• ?Ir is the gradient in the restored image.

• Assuming a linear camera response function
• An object is composed of edges, r becomes
• where ?Lthreshold would be given by Adrians
  model.
• Finally, we have

Hautière N, Dumont E (2007). Assessment of
visibility in complex road scenes using digital
imaging. In The 26th session of the CIE
(CIE07), Beijing, China.
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Visual Properties of Fog
Daylight
Atmospheric veil
Direct transmission
Scattering

• Koschmieders law gives the apparent luminance L
  of an object located at distance d to the
  luminance L0 measured close to this object
• where L8 is the atmospheric luminance and ß is
  the extinction coefficient of fog.

• Duntley developed a contrast attenuation law
• The CIE defined a standard dimension called
  meteorological visibility distance

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Contrast Restoration Fog Density Estimation

• Assuming a linear camera response function,
  Koschmieders law becomes in the image plane
• Assuming a flat world scene, it is possible to
  estimate (ß, A8) thanks to the existence of an
  inflection point on this curve
• where ? depends on camera parameters and vh
  denotes the horizon line.

Hautière N, Tarel JP, Lavenant J, Aubert D
(2006b). Automatic Fog Detection and Estimation
of Visibility Distance through use of an Onboard
Camera. Machine Vision and Applications Journal
17820.
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Contrast Restoration Principle

• To restore the contrast, we propose to reverse
  Koschmieders law. In this way, R can be
  estimated directly for all scene points from
• The remaining problem is the depth d of each
  pixel. For pixels not belonging to the sky
  region, i.e IltA8, a scene model is proposed
• d1 models the depth of pixels belonging to the
  road plane and d2 models the depth of the
  vertical surroundings.
• where c is a clipping plane, ? gt ? controls the
  relative importance of the flat world against the
  vertical surroundings.

u
v

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Contrast Restoration Algorithm

• One method aims at restoring the contrast of the
  road surface, while enhancing contrast on
  vertical objects without distorting them.
• We seek the best scene maximizes the contrast and
  minimizes the number of distorted pixels, i.e.
  the optimal values of ? and c.
• The problem can be formulated as a minimization
  process
• where Q is an image quality attribute, the norm
  of the local normalized correlation between the
  original image I and the restored image R

Hautière N, Tarel JP, Aubert D (2007). Towards
fog-free in-vehicle vision systems through
contrast restoration. In IEEE Computer Society
Conference on Computer Vision and Pattern
Recognition (CVPR07), Minneapolis, USA.
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Contrast Restoration Results
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Visible Edges Segmentation Principle and
Implementation

• By fog, the visible edges are the set of edges
  having a local contrast above 5.
• LIP model (Jourlin and Pinoli, 2001) defined the
  contrast associated to a border F which separates
  two adjacent regions
• where C(x,y)(f) denotes the contrast between two
  pixels x and y of the image f

• To implement this definition of contrast,
  Köhlers segmentation method has been used
  (Köhler, 1981).
• Instead of using this method to binarize images,
  we use it to measure the contrast locally

Hautière N, Aubert D, Jourlin M (2006a).
Measurement of local contrast in images,
application to the measurement of visibility
distance through use of an onboard camera.
Traitement du Signal 2314558.
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Visible Edges Segmentation Results
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Restoration Assessement Final Results

• The computation of r enables thus to compute the
  increase of visibility level VL produced by the
  contrast restoration method.
• e denotes the percentage of new visible edges,
  i.e. Cgt5.

Proposed method
Histogram stretching
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Conclusion

• In this paper, we proposed
• An efficient contrast restoration method,
• A methodology to assess its performances by
  gradient ratioing at visible edges,
• A method to extract edges having a local contrast
  above 5 based on LIP model.
• In the future, we want to tackle
• The detection of other meteorological phenomena
  such as rain, night-fog,
• The restoration of other types of image
  degradation.