Recent studies have shown that major visibility degradation effects caused by haze can be corrected for by analyzing polarization-filtered images. The analysis is based on the fact that the path-radiance in the atmosphere (airlight) is often partially polarized. Thus, associating polarization with path-radiance enables its removal, as well as compensation for atmospheric attenuation. However, prior implementations of this method suffered from several problems. First, they were based on mechanical polarizers, which are slow and rely on moving part. Second, the method had failed in image areas corresponding to specular objects, such as water bodies (lakes) and shiny construction materials (e.g., windows). The reason for this stems from the fact that specular objects reflect partially polarized
light, confusing a naive association of polarization solely with path-radiance. Finally, prior implementations derived necessary polarization parameters by manually selecting reference points in the field of view. This human intervention is a drawback, since we would rather automate the process. In this paper, we report our most recent progress in the development of our visibility-improvement method. We show directions by which those problems can be overcome. Specifically, we added algorithmic steps which automatically extract the polarization parameters needed, and make visibility recovery more robust to polarization effects originating from specular objects. In addition, we now test an electrically-switchable polarizer based on a liquid crystal device for improving acquisition speed.