Locally adaptive multiscale contrast optimization

Nicolas Bonnier, Eero P. Simoncelli

Research output: Chapter in Book/Report/Conference proceedingConference contribution


We describe a method for automatically and adaptively boosting the visibility of local features in an image. A log intensity image is fi rst decomposed into a set of subbands at multiple scales and orientations. Operating successively from coarse frequency bands to fi ne, the coeffi cients of each subband are adjusted so as to move their locally averaged amplitudes toward a target value using a gamma operation. Target values are chosen to fall linearly over scale, consistent with a scale-invariant spectral model. To avoid enlarging the range of image intensity values, in those locations where the local mean is near the minimal or maximal values of the image and the local contrast is being boosted signifi cantly, the local mean is moved toward the global mean. Finally, a spatial mask is applied in the pixel domain to ensure that the enhancements are applied only in the vicinity of image features. The resulting image appears to be both sharper and of higher contrast.

Original languageEnglish (US)
Title of host publicationIEEE International Conference on Image Processing 2005, ICIP 2005
Number of pages4
StatePublished - 2005
EventIEEE International Conference on Image Processing 2005, ICIP 2005 - Genova, Italy
Duration: Sep 11 2005Sep 14 2005

Publication series

NameProceedings - International Conference on Image Processing, ICIP
ISSN (Print)1522-4880


OtherIEEE International Conference on Image Processing 2005, ICIP 2005

ASJC Scopus subject areas

  • General Engineering


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