LIGHTNESS CONSTANCY FROM LUMINANCE CONTRAST

Abstract

To discount effects of uneven illumination we have designed and tested a neural network that can adaptively control light sensitivity at the photosensor level. Our neural network architecture simulates the ON channel response of the visual system using multiple layers of hexagonally arranged nodes having partially overlapping receptive fields of different spatial frequencies. Feedforward connections are excitatory while feedback pathways subserve lateral inhibition. The outputs of these nodes are combined so as to maximize the signal to noise ratio while providing constant feedback that resets photosensor thresholds to maintain high sensitivity. A sparse primitive interpolation technique was applied to the ensemble output of the sensitivity control module to determine if it sufficiently encodes surface reflectance. The motivation is to determine to what extent the ratio principle, as captured by the sensitivity control system, explains the lightness constancy phenomenon and whether information contained within an ON channel response is adequate to reconstruct the surface lightness. Our connectionist architecture can account for many characteristics attributed to the lightness constancy phenomenon observed in biological systems. The results suggest that our module maintains high sensitivity across a large range of intensities without interfering with the transmission of visual information embedded in the spatial discontinuities of intensity. However, the amplitude of the luminance derivative as encoded in ON channel responses is not sufficient to approximate surface reflectance.