Chaos-based image assessment for THz imagery

Abstract

Multiscale image processing is a powerful technique that can determine image characteristics (e.g. clutter), provide denoising, and determine object features. Imagery is highly nonstationary (i.e. mean and variance change with location and time) and multiscaled (i.e. dependent on the spatial or temporal interval lengths). In this paper, we utilize the scale-dependent Lyapunov exponent (SDLE), which unifies the principles of fractal and chaos theory, to characterize the different signal behaviors on a wide range of scales simultaneously. Commonly used complexity measures, including those from information theory, chaos theory, and random fractal theory, can all be related to the values of the SDLE at specific scales, and therefore, SDLE can act as the basis for a unified theory of multiscale analysis of complex imagery data. We describe the power-law and singular-value decomposition (SVD) for image processing and demonstrate a SDLE example using TeraHertz (THz) imagery for concealed target image fusion.