Sylvain Gigan (also in Science) let me know of the following paper that is also making the rounds in Science. If you recall, it was shown earlier that compressive sensing was speeding up the process of acquiring the data. The physics itself is not changed and revolves around this formula:
3D Computational Ghost Imaging by Baoqing Sun, Matthew P. Edgar, Richard Bowman, Liberty E. Vittert,Stephen S. Welsh, Ardrian Bowman, Miles J. Padgett
Computational ghost imaging retrieves the spatial information of a scene using a single pixel detector. By projecting a series of known random patterns and measuring the back reflected intensity for each one, it is possible to reconstruct a 2D image of the scene. In this work we overcome previous limitations of computational ghost imaging and capture the 3D spatial form of an object by using several single pixel detectors in different locations. From each detector we derive a 2D image of the object that appears to be illuminated from a different direction, using only a single digital projector as illumination. Comparing the shading of the images allows the surface gradient and hence the 3D form of the object to be reconstructed. We compare our result to that obtained from a stereo- photogrammetric system utilizing multiple high resolution cameras. Our low cost approach is compatible with consumer applications and can readily be extended to non-visible wavebands.The 3D of this paper has little to do with the 2D extraction from Three-dimensional ghost imaging ladar and we measure reflectance difference here as opposed to time of flight information. Here is the multispectral paper:
The field of ghost imaging encompasses systems which can retrieve the spatial information of an object through correlated measurements of a projected light eld, having spatial resolution, and the associated reflected or transmitted light intensity measured by a photodetector. By employing a digital light projector in a computational ghost imaging system with multiple spectrally fi ltered photodetectors we obtain high-quality multi-wavelength reconstructions of real macroscopic objects. We compare di erent reconstruction algorithms and reveal the use of compressive sensing techniques for achieving sub-Nyquist performance. Furthermore, we demonstrate the use of this technology in non-visible and fluorescence imaging applications.
Thanks Sylvain !
Liked this entry ? subscribe to Nuit Blanche's feed, there's more where that came from. You can also subscribe to Nuit Blanche by Email, explore the Big Picture in Compressive Sensing or the Matrix Factorization Jungle and join the conversations on compressive sensing, advanced matrix factorization and calibration issues on Linkedin.