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Spatio-Temporal Frequency Analysis for Removing Rain and Snow from Videos Carnegie Mellon University June 16, 2007 Peter Barnum Takeo Kanade Srinivasa.

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Presentation on theme: "Spatio-Temporal Frequency Analysis for Removing Rain and Snow from Videos Carnegie Mellon University June 16, 2007 Peter Barnum Takeo Kanade Srinivasa."— Presentation transcript:

1 Spatio-Temporal Frequency Analysis for Removing Rain and Snow from Videos Carnegie Mellon University June 16, 2007 Peter Barnum Takeo Kanade Srinivasa Narasimhan

2 Bad weather in outdoor videos Rain Snow

3 Previous Work Garg and Nayar CVPR ‘04 Garg and Nayar ICCV ‘05 Image-based blurring Streak detection Hase et al. ICIP ’98 Starik and Werman IWTAS ‘03 Zhang et al. ICME ‘06 Camera-based blurring Pixel Intensity Time Spikes due to rain

4 Groups of streaks

5 Imaging a falling particle Raindrops Gaussians Snowflakes Breadth Length

6 Gaussian model of streak appearance A Gaussian Including streak orientation (just a coordinate space rotation) A blurred Gaussian streak Camera parameters are constant

7 Where are the streaks?

8 Fourier transform of the streaks

9 Building a complete model Blurred Gaussian For all depths that are in-focus For a given precipitation intensity For all common drop sizes

10 Model accuracy Original image 2D Fourier Transform Model Model with 50% randomly set to zero

11 Finding the precipitation rate Rain and snow have two useful properties Large frame-to-frame difference Distributed evenly in frequency space Mailbox Building Snow 0% 100%

12 Frame-to-frame differences t=1 t=2 t=3 w=-1 w=0 w=+1

13 Frame-to-frame differences t=1 t=2 t=3 w=-1 w=0 w=+1

14 Finding the precipitation rate For most objects But for rain and snow Because of these properties

15 Estimating the model parameters The precipitation rate is approximately: Precipitation rate Streak orientation The orientation is found by: Estimating the streak orientation requires a spatial consistent estimate

16 Original image 2D FT Model Frequency space examples

17 Computing per-frequency estimates At a given frequency: =

18 Computing per-pixel estimates

19 Refining the single frame estimate Exactly the same model, constant in w t=1 t=2 t=3

20 Computing per-pixel estimates

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24 “Into each life some rain must fall.” -Henry Wadsworth Longfellow Conclusions and future work Refining global estimates with local features A global frequency method for rain and snow removal

25 Extra slides

26 Imaging a falling particle


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