When Does Computational Imaging Improve Performance?

If there is something to be said about compressive sensing is that it provides a new landscape when exploring parameters in imaging and other areas of signal processing. The technology that is getting the most press in imaging and so we are really in need of a study that explores phase space in which compressive sensing is interesting and instances where it is .... less interesting compared to direct imaging. To help in this exploration here is When Does Computational Imaging Improve Performance? by Oliver Cossairt, Mohit Gupta, and Shree K. Nayar (the pdf is here)

The abstract reads:

A number of computational imaging techniques have been introduced to improve image quality by increasing light throughput. These techniques use optical coding to measure a stronger signal level. However, the performance of these techniques is limited by the decoding step, which amplifies noise. While it is well understood that optical coding can increase performance at low light levels, little is known about the quantitative performance advantage of computational imaging in general settings. In this paper, we derive the performance bounds for various computational imaging techniques. We then discuss the implications of these bounds for several real-world scenarios (illumination conditions, scene properties and sensor noise characteristics). Our results show that computational imaging techniques do not provide a significant performance advantage when imaging with illumination brighter than typical daylight. These results can be readily used by practitioners to design the most suitable imaging systems given the application at hand.

and the magic formula is

There are obvious caveats to this but this is a very nice work and much appreciated contribution.




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