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Abstract:
Image processing often involves an image transformation into a
domain that is better correlated with visual perception, such as the
wavelet domain, image pyramids, multi-scale contrast
representations, contrast in retinex algorithms, and chroma,
lightness and colorfulness predictors in color appearance models.
Many of these transformations are not ideally suited for image
processing that significantly modifies an image. For example, the
modification of a single band in a multi-scale model leads to an
unrealistic image with severe halo artifacts. Inspired by gradient
domain methods we derive a framework that imposes constraints on the
entire set of contrasts in an image for a full range of spatial
frequencies. This way, even severe image modifications do not
reverse the polarity of contrast. The strengths of the framework are
demonstrated by aggressive contrast enhancement and a visually
appealing tone mapping which does not introduce artifacts.
Additionally, we perceptually linearize contrast magnitudes using a
custom transducer function. The transducer function has been derived
especially for the purpose of HDR images, based on the contrast
discrimination measurements for high contrast stimuli.
