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Psych 221/EE362 Final Project Presentation Gradient-Domain Dynamic Range Compression

Psych 221/EE362 Final Project Presentation Gradient-Domain Dynamic Range Compression. Emilio Antunez Thursday, March 13, 2003. Introduction. High dynamic range images increasingly common Dynamic range of common display technologies often inadequate

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Psych 221/EE362 Final Project Presentation Gradient-Domain Dynamic Range Compression

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  1. Psych 221/EE362 Final Project PresentationGradient-Domain Dynamic Range Compression Emilio Antunez Thursday, March 13, 2003

  2. Introduction • High dynamic range images increasingly common • Dynamic range of common display technologies often inadequate • We want to intelligently reduce dynamic range for best image quality possible

  3. Gradient-Domain HDR compression • Shrinks large gradients more aggressively than small gradients • Goal is to reduce global contrast ratios, while preserving local ones • Avoids halo artifacts found in other multiresolution techniques

  4. Algorithm Step I:Form logarithmic image, H(x,y) • Rough approximation of perceived brightness • Gradients in the logarithmic domain correspond to ratios in the luminance domain

  5. Algorithm Step II:Gradient Attenuation Function,  • Examines image at various resolutions • Reduces large gradients more than small ones • Subject to parameters , 

  6. Algorithm Step III:Reduced Gradient Field, G = H • Multiply logarithmic image gradient field by attenuation map • Result is generally not a conservative field

  7. Algorithm Step IV:Calculate Optimal Image, I • Can find the logarithmic image whose gradient field has minimal MSE with G • Use rapid Poisson solver to solve 2I = divG

  8. Algorithm Step V:Return to Luminance Domain • Normalize logarithmic image to be negative • Lout = eI

  9. Algorithm Step VI:Estimate New Image Colors • Cout = (Cin/Lin)sLout • s is generally between 0.4 and 0.6

  10. Results • Resulting images have significantly lower dynamic ranges • 1280x1024 image: MATLAB implementation takes just over a minute on ISE machines • Results vary depending on values chosen for , , s

  11. Results“Belgium House” Image Dynamic Range Conversion: 5.79 1.08 log units

  12. Results“SunShadePerson” Image Dynamic Range Conversion: 2.67 0.73 log units

  13. Results“OfficeLightsOnPerson” Image Dynamic Range Conversion: 3.65  0.67 log units

  14. Results“OfficeLightsOffPerson” Image Dynamic Range Conversion: 3.76 0.77 log units

  15. Conclusions • Algorithm is very effective at preserving detail • Would like to see algorithms that reduce the need for hand-tweaking

  16. Acknowledgements • Thanks to Feng Xiao for images, advice

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