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Removing Weather Effects from Monochrome Images. Srinivasa Narasimhan and Shree Nayar Computer Science Department Columbia University IEEE CVPR Conference December 2001, Hawaii, USA Sponsors DARPA HID, NSF. Contrast Degradation in Bad Weather. Fog. Rain.

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slide1

Removing Weather Effects from

Monochrome Images

Srinivasa Narasimhan and Shree Nayar

Computer Science Department

Columbia University

IEEE CVPR Conference

December 2001, Hawaii, USA

Sponsors

DARPA HID, NSF

slide2

Contrast Degradation in Bad Weather

Fog

Rain

How does scene contrast degrade in bad weather ?

How can scene contrast be restored from bad weather images ?

slide3

Image Processing Does Not Suffice

Histogram Equalized Images

Weather Effects are Depth Dependent

slide4

Prior Methods for Contrast Restoration

Weather

Information

Clear-day Scene

Intensity/Color

Method

Scene Depth

Predicted

Weather PSF

Required

Computed

Yitzhaky, Kopeika [98]

Gaussian

distribution

assumed

Oakley, Tan, Satherley

[98,01]

Not Required

Required

Wavelength

Independent

Scattering

Nayar, Narasimhan [99]

Narasimhan, Nayar [00]

Computed

(Color Images Required)

Computed

Not Required

Computed

Computed

OUR GOAL :

slide5

Scattering Models : AttenuationandAirlight

Diffuse

Skylight

Sunlight

Object

Observer

d

Diffuse

Ground Light

Attenuation

Airlight

slide6

Contrast Degradation in Bad Weather

Irradiance = Attenuation + Airlight

=

+

Scattering

Coefficient

Reflectance

Horizon

Brightness

Depth

(1)

(2)

Contrast between Iso-Depth points , P and P :

Contrast Decay : Exponential in Scene Depth

slide7

Depth Edges vs. Reflectance Edges

Mild Fog

Denser Fog

Reflectance Edge

Depth Edge

Normalized SSD of

Depth Edge Neighborhood

Normalized SSD of

Reflectance Edge Neighborhood

slide8

Edge Classification from Weather Changes

Edge Classification

Mild Fog

Denser Fog

Reflectance Edge :

Depth Edge :

slide9

Scene Structure from Weather Changes

Irradiance under versus Irradiance under : Linear

All Scene points

at Depth 1

All Scene points

at Depth 2

Scaled Depth :

slide10

Depth Map from Two Weather Conditions

Mild Fog, 5 PM (Input)

Computed Depth Map (Output)

  • Comparing with Prior Methods:
    • Color Images Not Needed
    • Works for Wider Range of
    • Weather Conditions

Denser Fog , 5: 30 PM (Input)

slide11

Weather Removal Using Scene Structure

Dense Fog, 5:30 PM (Input)

Contrast Restored Image (Output)

Computed Depth Map (Input)

Histogram Equalized Image (For comparison)

slide13

A De-Weathering System

System Initialization : Computing Scene Structure

Video Frame

(Weather 1)

Detect

Significant

Weather

Change

Scene

Structure

Video Frame

(Weather 2)

Continuous De-Weathering Using Scene Structure

Contrast

Restored

Video Frame

Scene

Structure