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Within the last few years, cellphone subscriptions have widely spread and now cover even the remotest parts of the planet. Adequate access to healthcare, however, is not widely available, especially in developing countries. We propose a new approach to converting cellphones into low-cost scientific devices for microscopy. Cellphone microscopes have the potential to revolutionize health-related screening and analysis for a variety of applications, including blood and water tests. Our optical system is more flexible than previously proposed mobile microscopes and allows for wide field of view panoramic imaging, the acquisition of parallax, and coded background illumination, which optically enhances the contrast of transparent and refractive specimens.

3D content and display technology is now widely available. However, available displays range from large-screen cinematic projection systems to hand-held devices and from screens supporting glasses-free 3D modes to glasses-bound systems. Counterintuitively, the produced content is usually only generated for a single display configuration, making labor-intense, manual post-processing of the data necessary. Recently, several content remapping techniques for stereo content have been proposed (see e.g. [Lang et al. 2010]). We present a perceptually-driven optimization framework for automatic light field remapping, specially designed for automultiscopic displays. It takes into account both the limitations of the target display as well as those of a human observer, and poses the problem as a non-linear least squares optimization. Our model includes depth of field limitations and the contrast sensitivity function, as well as sensitivities to binocular disparity and motion parallax in the perception of depth. Additionally, the model can be adapted for more common stereo remapping.

Conventional radiometric compensation approaches such as [Bimber et al. 2005] rely on a direct mapping between projector and camera pixels. Global illumination effects such as reflections, refractions, scattering, dispersion, diffraction etc are completely ignored since only local illumination is taken into account. We propose a novel approach that applies the light transport matrix for performing an image-based radiometric compensation that accounts for all possible lighting effects.