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.

Citations