A Computational Darkroom for BW Photography Soonmin Bae, Sylvain Paris, and Frédo 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”

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

What can be the “Look”

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

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

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

What they do vs. What we do 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 Objective tone reproduction vs. Control of the look Non-parametric vs. Parametric characterization

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

Quick Technical Overview large scale Challenge: differentiate texture from edges. “textureness” input detail

Quick Technical Overview Histogram manipulation (transfer possible)

Quick Technical Overview Histogram manipulation of the “textureness”

Quick Technical Overview before after

Exploring Various Options in a Few Clicks

Preliminary Results Model Input

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