Abstract: In a method of chromagenic illuminant estimation pixels from mutually-corresponding images with different filtering (e.g. a filtered image and an unfiltered image) are compared, a fraction of the brightest pixels being selected for a subsequent chromagenic estimation. The pixels may be at corresponding locations or they may correspond in that their mean brightness is in the same rank order. In one method, in which, in a first preprocessing stage, for a database of m lights Ei (?) and n surfaces Sj (?) there is calculated Ti˜QF Q+ where Q1F and QF represent the matrices of unfiltered and filtered sensor responses to the n surfaces under the i th light and + denotes an inverse, and in a second operation stage, given P surfaces in an image and 3×P matrices Q and QF, from these matrices there are chosen the r% brightest pixels giving the matrices Q? and Q?F, and the scene illuminant Pest is estimated where formula (I) and (II).

Abstract: An image having m light sources, with m preferably equaling 2 or 3, is sequenced into different regions, each of which is lit by only one of the m light sources, by obtaining paired imaged with different filtering, for example a filtered and an unfiltered image, applying to the image pairs sets of m pre-computed mappings at the pixel or region level, and selecting the most appropriate. The rendering of the information in the image maybe adjusted accordingly.

Abstract: The probability of a chromaticity being a white point of image scene illumination is estimated by convolving a chromaticity histogram with a function of log probability distribution of chromaticities under an illuminant.

Abstract: A white point of scene illumination is determined by using a chromaticity histogram that is weighted for brightness.

Abstract: The probability of a chromaticity being a white point of image scene illumination is estimated by convolving a chromaticity histogram with a function of log probability distribution of chromaticities under an illuminant.