Enhancing images with intensity-dependent spread functions

Abstract

The theory of Intensity-Dependent Spread functions (IDS) is a model of the human visual system. The motivation behind IDS is to balance resolution and reliability. The system does this, but it also predicts many phenomena in human vision. IDS is a nonlinear, adaptive system that enhances edges, nonlinearly compresses dynamic ranges and automatically adjusts to local variations in intensity. It has a single free parameter, uses only additions in its on-line calculations and can be performed on a parallel processor. Another property of the system is that for inputs with only two intensities, e.g. disks, square waves and step edges, the output reduces exactly to one plus the convolution of the input with a bandpass filter whose passband is determined by the two intensities. For a Gaussian spread function the transfer function becomes the Difference-of-Gaussians (DoG) filter but the bandwidth set automatically by the input intensities. This paper demonstrates how IDS can be used for digital image enhancement. There is an artificial image that illustrates the characteristics of IDS processing and shows how the theoretical results translate into visual effects. There are also several realistic scenes that have been enhanced by IDS.