Polarization-Based Decorrelation of Transparent Layers: The Inclination Angle of an Invisible Surface

Abstract

When a transparent surface is present between an observer and an object, an image reflected by the surface may be superimposed on the image of the observed object. We present a new approach to recover the scenes (layers) and to classify which is the reflected/transmitted one, based on imaging through a polarizing filter at two orientations. Estimates of the separate layers are obtained by weighted pixel-wise differences of these images, inverting the image formation process. However the weights depend on the angle of incidence, hence on the inclination of the transparent (invisible) surface. This angle is estimated by seeking the angle-value which (through the weights) leads to decorrelation of the estimated layers. Experimental results, obtained using real photos of actual objects, demonstrate the success of angle estimation and consequent layer separation and labeling. The method is shown to be superior to earlier methods where only raw optical data was used.