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Comparametric equations with practical applications in quantigraphic image processing.

S Mann1

  • 1Department of Electrical and Computer Engineering, University of Toronto, Toronto, Ont., M5S 3G4, Canada.

IEEE Transactions on Image Processing : a Publication of the IEEE Signal Processing Society
|February 12, 2008
PubMed
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This study introduces quantigraphic image processing, proposing an antihomomorphic filter to cancel implicit homomorphic filters in cameras. This new framework offers improved exposure adjustment alternatives to flawed gamma correction.

Area of Science:

  • Image Processing
  • Computer Vision
  • Digital Photography

Background:

  • Implicit homomorphic filters are present in standard camera signal processing pipelines.
  • While often beneficial, these filters can be undesirable in certain imaging applications.

Purpose of the Study:

  • To propose the cancellation of implicit homomorphic filters using an antihomomorphic filter.
  • To introduce the principle of quantigraphic image processing and comparametric equations.
  • To present alternatives to gamma correction for image exposure adjustment.

Main Methods:

  • Modeling cameras as arrays of light meters responding to light quantity.
  • Introducing comparametric equations for analyzing images with varying exposures.
  • Developing new processing methods in the "amplitude domain".

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Main Results:

  • Demonstrating that gamma correction is flawed as its underlying function does not pass through the origin.
  • Presenting alternative methods for exposure adjustment that address the flaws of gamma correction.
  • Establishing a theoretical framework applicable to most modern cameras.

Conclusions:

  • Quantigraphic image processing offers a new paradigm for image analysis and manipulation.
  • The proposed antihomomorphic filter and amplitude domain processing provide more accurate control over image exposure.
  • The findings challenge conventional image processing techniques like gamma correction.