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A Computational Darkroom for BW Photography

A Computational Darkroom for BW Photography. Soonmin Bae, Sylvain Paris, and Fr é do Durand Current Status : Resubmission to Siggraph. Objectives. To enhance black-and-white photographs “Look” transfer between two images Direct interpolation and manipulation of the “look”.

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A Computational Darkroom for BW Photography

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  1. A Computational Darkroom for BW Photography Soonmin Bae, Sylvain Paris, and Frédo Durand Current Status : Resubmission to Siggraph

  2. Objectives • To enhance black-and-white photographs • “Look” transfer between two images • Direct interpolation and manipulation of the “look”

  3. What is the problem? Direct conversion to B&W yields often unsatisfying results.

  4. What can be the “Look”

  5. Approaches • Decomposition of an image into large-scale variation layer and high frequency texture layer • Control the global contrast and the local textureness separately • Quantitative characterization • Use image statistics and histograms

  6. What we aim at… • Control of the visual quality, “look” • Parametric characterization • User-oriented and intuitive method • HDR images

  7. What we do not do… • Deal with • Color photographs • Paintings • Change Content • Change Composition • Crop • Select a model or ideal parameters

  8. What they do vs. What we do • Objective tone reproduction vs. Control of the look • Non-parametric vs. Parametric characterization • Tone mapping • Ferwerda et al. 1996;Tumblin and Rushmeier 1993; Ward 1994 • Ashikhmin 2002; Tumblin and Turk 1999; Pattanaik et al. 1998; Reinhard et al. 2002 • Color2gray • Gooch et al. 2005 • Image analogies • Hertzmann et al. 2001; Efros and Freeman 2001; Rosales et al. 2003; Drori et al. 2003

  9. Challenges • Identification of important visual characteristics • Meaningful feature selection • Decomposition • Faithful extraction of the features • Reconstruction • Visual artifact (mainly halos) • Subjective issues • Preference vs. Similarity

  10. Quick Technical Overview large scale Challenge: differentiate texturefrom edges. “textureness” input detail

  11. Quick Technical Overview Histogram manipulation (transfer possible)

  12. Quick Technical Overview Histogram manipulation of the “textureness”

  13. Quick Technical Overview after before

  14. Exploring Various Options in a Few Clicks

  15. Preliminary Results Model Input

  16. Open Discussion • Should we include the following domains? • Color photographs • Paintings • Which should be pursued? • Transfer vs. Direct parameter modification • Similarity vs. Preference

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