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.
Outdoor imaging in haze is plagued by poor visibility. A major problem is spatially-varying reduction of contrast by airlight, which is scattered by the haze particles towards the camera. However, images can be compensated for haze, and even yield a depth map of the scene. A key step in such scene recovery is subtraction of the airlight. In particular, this can be achieved by analyzing polarization-filtered images. This analysis requires parameters of the airlight, particularly its degree of polarization (DOP). These parameters were estimated in past studies by measuring pixels in sky areas. However, the sky is often unseen in the field of view. This paper derives several methods for estimating these parameters, when the sky is not in view. The methods are based on minor prior knowledge about a couple of scene points. Moreover, we propose blind estimation of the DOP, based on the image data. This estimation is based on independent component analysis (ICA). The methods were demonstrated in field experiments.
Simbionix® Ltd, world-leader in surgical simulation, just pioneered the commercial use of patient-specific simulation: a tool for rehearsing an operation prior to performing it. Carotid stenting was chosen as the first release of a patient-specific simulated operation. Simbionix partnered with Shina Systems® in making the Segmentation module. The resulting tool is presented and discussed herein.
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