Computational Imaging

April 28, 2016 Melanie Kiefer

The theme of today's blog post is computational imaging. Computational imaging is the systematic enhancement of computer-aided imaging by treating algorithmics, sensors and optics as a single entity and optimizing them.

Computational imaging is an emerging interdisciplinary field of research. Unlike optics, which is a traditional branch of physics, computational imaging is rooted at the point of intersection between the STEM fields (science, technology, engineering and mathematics). The goal is to bring together the 'best' elements from optics, image processing, physics, mathematics and computer science to forge new frontiers in imaging.

This means both trying to circumvent classic limitations, e.g. Abbe's resolution limit, as well as enabling brand new imaging, e.g. in 3D. The large number of application fields and the variety of methods make computational imaging a very heterogeneous field of research which requires experts from extremely different disciplines to collaborate.
Here's a good example of the unconventional fusion of algorithmics and optics: modern tomographic methods. These are commonplace in medical technology, but tomographic methods are also used to reconstruct for example 3D profiles of nebulae in outer space and to measure air currents in 3D using camera arrays, e.g. in wind tunnels.

Computational Imaging
Image 1: a camera array for measuring air currents. Left: optical distortions (streaks or 'schlieren') are measured by capturing images of a patterned background through the distortion using algorithmics. Middle: resulting tomographic reconstruction of the density field of the distortion. Right: a wind tunnel in use (image property of ONERA). You can see the camera array in the background and a nozzle with a digitally superimposed visualization of the current in the foreground.

 

In the following blog entries, we'll have a closer look at two characteristic examples of how computational imaging technologies are applied to microscopy. We've chosen two very different examples falling into the framework of computational imaging to illustrate the breadth of this technology and development trends.

First we want to give you a look at digital microscopy. This field remains strongly focused on classical optics, and computational imaging is used primarily to support and improve digital microscopy. The optical design is still based on that of a traditional microscope and has not been fundamentally modified.

For our second example, we'll have a look at ptychography. This technique has abandoned the traditional n-fold magnified image which you'd expect in microscopy in favor of an 'algorithmic' or coded image which can only be decoded using mathematical methods. In other words: information for the object being observed is no longer directly accessible to the human eye. Instead, we rely on computer science to extract and display the image. The benefit of this approach is a large, imaged field with a simultaneously high magnification.

At the end we'll talk about light field technologies which make it possible to digitally alter traditional physical parameters of an image – such as the selection of the focal plane – all of which is done after the image has been captured.

Lars Omlor and Ivo Ihrke

 

References:

Atcheson, B., Ihrke, I., Heidrich, W., Tevs, A., Bradley, D., Magnor, M., & Seidel, H. P. (2008, December). Time-resolved 3d capture of non-stationary gas flows. In ACM transactions on graphics (TOG) (Vol. 27, No. 5, p. 132). ACM.

M. L. Faulkner and J. M. Rodenburg. Movable aperture lensless transmission microscopy: A novel phase retrieval algorithm. Phys. Rev. Lett., 93:023903, 2004.

Levoy, M., & Hanrahan, P. (1996, August). Light field rendering. In Proceedings of the 23rd annual conference on Computer graphics and interactive techniques (pp. 31-42). ACM.

Levoy, M., Ng, R., Adams, A., Footer, M., & Horowitz, M. (2006). Light field microscopy. ACM Transactions on Graphics (TOG), 25(3), 924-934.

Mignard-Debise, L., & Ihrke, I. (2015, October). Light-field Microscopy with a Consumer Light-field Camera. In 3D Vision (3DV), 2015 International Conference on (pp. 335-343). IEEE.

Tian, J. Wang, and L. Waller, "3D differential phase-contrast microscopy with computational illumination using an LED array," Optics Letters, vol. 39, no. 5, pp. 1326--1329, March 2014.

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