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Practical Scene Illuminant Estimation via Flash/No-Flash Pairs

Practical Scene Illuminant Estimation via Flash/No-Flash Pairs. Cheng Lu and Mark S. Drew Simon Fraser University {clu, mark}@cs.sfu.ca. Flash/No-flash Imagery – a Brief History. diCarlo, Xiao, & Wandell, CIC 2001. Combine flash/no-flash images to produce a pure-flash image.

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Practical Scene Illuminant Estimation via Flash/No-Flash Pairs

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  1. Practical Scene Illuminant Estimation via Flash/No-Flash Pairs Cheng Lu and Mark S. Drew Simon Fraser University {clu, mark}@cs.sfu.ca

  2. Flash/No-flash Imagery – a Brief History diCarlo, Xiao, & Wandell, CIC 2001 • Combine flash/no-flash images to • produce a pure-flash image. • Use dim=3 FDM + knowledge of flash SPD and sensor curves to estimate surface reflectance •  most likely ambient illuminant Szeliski et al., Siggraph 2004 Transfer lower-noise information from flash image to higher-noise ambient-light image. Raskar et al., Non-Realistic Rendering 2004 Filling in night-time imagery with daytime image info. Blake et al., Poisson Image Editing, Siggraph 2004 Drew,Lu,Finlayson, Removing Shadows using Flash/Noflash Image Edges , ICME 2006 Copy edges from cloned image region into edge-map of target background; re-integrate. Find shadow-mask, copy edges inside shadow from flash image into ambient image, re-integrate.

  3. Like diCarlo&Wandell approach, but replace knowledge of camera sensor curves with a camera RGB-based calibration using difference of with-flash and no-flash images. How? • Spectral sharpening • Subtract “both” – “no-flash”  pure-flash image • Log’s • Project difference of flash minus ambient into • geometric-mean chromaticity color space  Calibrate such to get illuminant chromaticity. This paper: Estimate Ambient Illuminant, using Flash/No-flash Pairs

  4. What’s the point?: Can estimate scene (ambient) illuminant without knowing: • Flash SPD • Camera sensors • Surface reflectance

  5. Why estimate the illuminant? White balance, plus many computer vision applications == intrinsic images without illumination. What’s good about this method? • Simple • Fast

  6. + The set-up: 2 images , one under ambient lighting, & another under flash. Under Ambient: Image “A”. Under Both: Image “B”.

  7. - ( + ) = The Key: Pure-Flash Image • The ambient light from “A” is also in “B”. • Therefore if we subtract the two, we have “F”: the pure-flash image. Under Flash: Image “F”:

  8. Image “F”: the scene as imaged under Flash light only. Incidentally, note that there are now extra shadows, from the flash (since it’s offset from the lens).

  9. Simple Image Formation Model  will guide us. Assumptions: 1., 2., 3. 1. Lambertian surface: RGB = Shading = normal  effective light-direction Illuminant Surface Sensors

  10. 2. Narrow-band sensors: is exactly a single-spike sensor: so then

  11. 3. Planckian light: (in Wien’s approximation) But, can violate 1., 2., 3. and still succeed. Gives

  12. Now take Log’s, to pull apart multiplications: Camera-dep’t vector Camera-dep’t vector Intensity and shading Surface Color-temperature of light where

  13. We’d like to remove intensity/shading term: So form geometric-mean chromaticity: In logs: Surface Camera-dep’t vector where Color-temperature of light

  14. The point: As temp (light color) changes, move along straight line. • But, we have “A” and “F” images: • Subract them, and use same chromaticity trick •  Only illumination is left!

  15. Log-difference Geometric-Mean Chromaticity  { • So log-log delivers inverse-temperature difference: • Calibrate for 1/TA-1/TF, • then in new scene obtain TA!

  16. What does this look like? Moved to 2D; color-matching functions in geo-mean chromaticity. (9 Planckians, Macbeth ColorChecker, spike sensors, xenon flash SPD)

  17. Sony DXC930 sensors, Daylights+F2, actual xenon flash SPD: • How to proceed: • Sharpen • Find closest cluster “Reference locus”

  18. Effect of sharpening: Kodak DCS420: Poor clusters  Better clusters #’ing

  19. Test: can we determine the illuminant? 102 illuminants, Sony camera, Macbeth patches 102 illuminants, Sony camera, Munsell patches Estimate illum. from Munsell to Macbeth  Nearly 100% correctly identified.

  20. Application: White Balance Image under CWF; CWF+Xenon No flash  4 calibration illuminants, HP camera, Macbeth chart (each cluster has 24 dots) With flash  • Sharpen • Sample image at 24 locations • evenly over image • Same (“daylight”) color balance • for training and for testing

  21. Overlaps best with CWF, so use white patch of Macbeth under CWF for white balance: Our color-balance– Much closer. “Auto” balance – Wrong. “Fluor” balance – Correct.

  22. Thanks! To Natural Sciences and Engineering Research Council of Canada

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