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Removing Weather Effects from Monochrome Images

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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Removing Weather Effects from Monochrome Images

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  1. 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

  2. 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 ?

  3. Image Processing Does Not Suffice Histogram Equalized Images Weather Effects are Depth Dependent

  4. 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 :

  5. Scattering Models : AttenuationandAirlight Diffuse Skylight Sunlight Object Observer d Diffuse Ground Light Attenuation Airlight

  6. 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

  7. 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

  8. Edge Classification from Weather Changes Edge Classification Mild Fog Denser Fog Reflectance Edge : Depth Edge :

  9. 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 :

  10. 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)

  11. Weather Removal Using Scene Structure Dense Fog, 5:30 PM (Input) Contrast Restored Image (Output) Computed Depth Map (Input) Histogram Equalized Image (For comparison)

  12. Different amounts of Fog removed from different Depths

  13. 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

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