A computational darkroom for bw photography
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A Computational Darkroom for BW Photography PowerPoint PPT Presentation

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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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.


What can be the look

What can be the “Look”


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”


Quick technical overview3

Quick Technical Overview

after

before


Exploring various options in a few clicks

Exploring Various Options in a Few Clicks


Preliminary results

Preliminary Results

Model

Input


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


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