I am spending three days visiting the group of Bernhard Schölkopf in Tübingen to discuss possible overlaps between his work on computational photography and image processing and current problems in astrophysics. Wow, there is a lot of overlap! In particular, his group has solved a problem I am interested in: How to combine lucky imaging
data (fast images, variable PSF) to get information at the highest possible resolution and signal-to-noise. The current schemes usually throw out less-good data, rather than combine it optimally. Here in Tübingen, Hirsch et al have something close to an optimal system. It is described here.
Rob Fergus is also visiting at the same time. In the afternoon he gave a talk about his work on blind deconvolution of natural images, using the statistics of image gradients as a handle on the point-spread function. All of astronomy is a form of blind deconvolution: The instrument (plus atmosphere) convolves what we care about with an unknown point-spread function; we must simultaneously figure out that function and what we care about. This is an ill-posed problem we solve using heuristics (like find the stars, estimate the PSF using them, then look at everything else assuming that PSF
), but something that truly does simultaneous inference will win in the end.
Tomorrow is supposed to be a hack day!
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