1 June 2007 Morphological multiscale contrast approach for gray and color images consistent with human visual perception
Ma Del Carmen Espino-Gudiño, Israel Santillan, Iván Ramon Terol-Villalobos
Author Affiliations +
Abstract
Some algorithms for morphological multiscale contrast enhancement for gray and color images, based on a psychophysical model of contrast perception, are introduced. The color space for applying the transformations is the space of chromatic coordinates (u,v,Y). The brightness Y of the space is transformed separately to increase its contrast; subsequently, the gravity center law is used to enhance hue. One of the algorithms applied to Y is based on a composition of contrast mappings that are built by means of the openings and closings by reconstruction. This composition successfully finds the principal regions according to a morphological contrast criterion. This contrast criterion is consistent with Weber's law. The second approach is derived from the Retinex approach that uses a family of Gaussian transformations. Instead of working with Gaussian transformations, a family of openings by reconstruction is used. Again, Weber's law is used for building the operators, although this law is not used as a criterion for selecting the primitives but as a concept for building rational morphological operators. For color images, once the brightness is enhanced, the color is improved by increasing the saturation of the image.
©(2007) Society of Photo-Optical Instrumentation Engineers (SPIE)
Ma Del Carmen Espino-Gudiño, Israel Santillan, and Iván Ramon Terol-Villalobos "Morphological multiscale contrast approach for gray and color images consistent with human visual perception," Optical Engineering 46(6), 067003 (1 June 2007). https://doi.org/10.1117/1.2749711
Published: 1 June 2007
Lens.org Logo
CITATIONS
Cited by 10 scholarly publications.
Advertisement
Advertisement
RIGHTS & PERMISSIONS
Get copyright permission  Get copyright permission on Copyright Marketplace
KEYWORDS
Image enhancement

Colorimetry

Visualization

Image filtering

Image processing

Optical engineering

Reconstruction algorithms

RELATED CONTENT


Back to Top