Gaussian mixture model based illumination normalization for global enhancement

Abstract

A method is presented for enhancing an image from an initial image, comprising computing a first luminance level frequency distribution corresponding to plurality of pixel constructing said initial image, representing said first luminance level frequency distribution as a resultant of Gaussian model mixtures to assess relative utilization of overall luminance level across said initial image, computing a desired luminance level frequency distribution as a function of the relative utilization, computing a transfer function to adjust the first luminance level frequency distribution to an enhanced luminance level as a function of desired level frequency distribution estimation, and applying said transfer function globally to said initial image to provide an enhanced image is. The first luminance level can be adapted to be linearized in a logarithmic form. In one embodiment, the model comprises one or more Gaussian functions. The initial image can be a background image estimated from a sequence of images.

Dependencies are inferred from the claim text.