Lightness and Retinex Theory

Abstract

Sensations of color show a strong correlation with reflectance, even though the amount of visible light reaching the eye depends on the product of reflectance and illumination. The visual system must achieve this remarkable result by a scheme that does not measure flux. Such a scheme is described as the basis of retinex theory. This theory assumes that there are three independent cone systems, each starting with a set of receptors peaking, respectively, in the long-, middle-, and short-wavelength regions of the visible spectrum. Each system forms a separate image of the world in terms of lightness that shows a strong correlation with reflectance within its particular band of wavelengths. These images are not mixed, but rather are compared to generate color sensations. The problem then becomes how the lightness of areas in these separate images can be independen t of flux. This article describes the mathematics of a lightness scheme that generates lightness numbers, the biologic correlate of reflectance, independent of the flux from objects INDEX HEADINGS: Vision; Color.

A model for contrast discrimination with incremental and decremental test patches

“…Probably with this assumption in mind the log transform is commonly employed to model the initial transduction stage in models of brightness and lightness constancy (e.g. Land & McCann, 1971).…”

Low-light image restoration using bright channel prior-based variational Retinex model

“…The bilateral filtering process can effectively reduce the noise while preserving the edge. The enhanced bright channel enables to estimate the optimal illumination component as a constraint of the regularized minimization in (10). Figure 1 shows the comparison of the bright channel used in Fu's method [19] and the proposed method.…”

“…On the other hand, Retinex-based methods are based on the human color perception system [10,11]. Provenzi et al mathematically analyzed the Retinex algorithm and demonstrated the performance according to various parameters such as threshold and the number of path of light paths to a pixel [11].…”

A neuromorphic model for achromatic and chromatic surface representation of natural images

“…The model predicts that the light source will be self-luminous (the peak goes above white, iJJ). The corresponding input taken by a camera uses the Highest-Luminance-As-White (HLAW) rule (Horn 1977;Land & McCann, 1971;Wallach, 1948Wallach, , 1976, and drove the background to be too dark to recognize. …”