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Removing Weather Effects from Monochrome Images Srinivasa Narasimhan and Shree Nayar Computer Science Department Columbia University IEEE CVPR Conference.

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Presentation on theme: "Removing Weather Effects from Monochrome Images Srinivasa Narasimhan and Shree Nayar Computer Science Department Columbia University IEEE CVPR Conference."— Presentation transcript:

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 How does scene contrast degrade in bad weather ? How can scene contrast be restored from bad weather images ? Contrast Degradation in Bad Weather RainFog

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

4 Prior Methods for Contrast Restoration Yitzhaky, Kopeika [98] Oakley, Tan, Satherley [98,01] Nayar, Narasimhan [99] Narasimhan, Nayar [00] Scene Depth Weather Information Required Predicted Weather PSF Required Computed Wavelength Independent Scattering Not Required Method Clear-day Scene Intensity/Color Computed Gaussian distribution assumed Computed (Color Images Required) OUR GOAL : ComputedNot Required Computed

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

6 Contrast Degradation in Bad Weather Irradiance = Attenuation + Airlight + = Horizon Brightness Depth Reflectance Scattering Coefficient Contrast Decay : Exponential in Scene Depth Contrast between Iso-Depth points, P and P : (1)(2)

7 Mild Fog Denser Fog Depth Edge Reflectance Edge Depth Edges vs. Reflectance Edges Normalized SSD of Reflectance Edge Neighborhood Normalized SSD of Depth Edge Neighborhood

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

9 Scene Structure from Weather Changes Irradiance under versus Irradiance under : Linear Scaled Depth : All Scene points at Depth 1 All Scene points at Depth 2

10 Depth Map from Two Weather Conditions Mild Fog, 5 PM (Input) Denser Fog, 5: 30 PM (Input) Computed Depth Map (Output) Comparing with Prior Methods: Color Images Not Needed Works for Wider Range of Weather Conditions

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

12 Different amounts of Fog removed from different Depths

13 A De-Weathering System Detect Significant Weather Change Video Frame (Weather 1) Video Frame (Weather 2) Scene Structure System Initialization : Computing Scene Structure Continuous De-Weathering Using Scene Structure Scene Structure Contrast Restored Video Frame


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