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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

A Computational Darkroom for BW Photography

Soonmin Bae, Sylvain Paris,

and Frédo Durand

Current Status : Resubmission to Siggraph

objectives
Objectives
  • To enhance black-and-white photographs
  • “Look” transfer between two images
  • Direct interpolation and manipulation of the “look”
what is the problem
What is the problem?

Direct conversion to B&W yields often unsatisfying results.

approaches
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
What we aim at…
  • Control of the visual quality, “look”
  • Parametric characterization
  • User-oriented and intuitive method
  • HDR images
what we do not do
What we do not do…
  • Deal with
    • Color photographs
    • Paintings
  • Change Content
    • Change Composition
    • Crop
  • Select a model or ideal parameters
what they do vs what we do
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
challenges
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
Quick Technical Overview

large scale

Challenge: differentiate texturefrom edges.

“textureness”

input

detail

quick technical overview1
Quick Technical Overview

Histogram manipulation (transfer possible)

quick technical overview2
Quick Technical Overview

Histogram manipulation of the “textureness”

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