A localized nonlinear method for the contrast enhancement of images

This paper presents a number of concepts that are important contributions to the area of contrast enhancement. Among them are the idea of gray scale partitioning, the use of a tunable cubic polynomial for the contrast enhancement function, and, in the case of noisy images, the use of a tandem of pyramidal lowpass filters to remove the noise from homogeneous regions (regions of constant or near-constant intensity) while simultaneously preserving the edges. Gray scale partitioning is a unique and original concept. This idea is based upon the human perception of a set of gray chips. The human perception was quantified by a set of ten intensity values that were converted to reflectances. The essence of gray scale partitioning is to transform these reflectances into gray scale intensity values (assuming constant illumination). These intensity values form the endpoints of discrete subintervals and are therefore the basis for gray scale partitioning. This ensures that the enhancement process will preserve shades of gray. That is, the output gray value will be of the same shade of gray (will lie in the same subinterval) as the input gray value