In the recent a few years the direction of computational photography has become hot, with unconventional cameras capturing not only a traditional image but also structure of the scene. Yet this might be the first paper to state a unified framework for such cameras.
The atomic element that interact camera sensors are light rays, which could be encoded by the notion of light field. With this notion, an image captured by a computational camera could be formulated as a linear projection of the light field. Considering the noises on the sensor, reconstruction of the light field could be addressed as solving a linear problem in the Bayesian manner.
A number of recent optical designs were examined in this framework for a empirical comparison, including pinhole, lens, wavefront coding, coded aperture, stereo and plenoptic cameras. It was found that a good depth prior, e.g. a mixture of oriented Gaussians, is critical for computational imaging tasks. It was also found that the optical design of wavefront coding is optimal to capture single-view scenes while a stereo configuration is best for capturing the full light field. Well, it's ironic to note that both configurations are quite "ancient": even earlier than the notion of light field was invented...
This paper might be helpful for vision people to design new cameras, and for setting up a more analytical study of existing designs. I personally think this could be a cornerstone of the field.
This is a eccv2008 paper and could be downloaded from:
http://people.csail.mit.edu/alevin/
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