> Very long baseline interferometry (VLBI) is a technique for imaging celestial radio emissions by simultaneously observing a source from telescopes distributed across Earth. The challenges in reconstructing images from fine angular resolution VLBI data are immense. The data is extremely sparse and noisy, thus requiring statistical image models such as those designed in the computer vision community. In this paper we present a novel Bayesian approach for VLBI image reconstruction. While other methods often require careful tuning and parameter selection for different types of data, our method (CHIRP) produces good results under different settings such as low SNR or extended emission. The success of our method is demonstrated on realistic synthetic experiments as well as publicly available real data. We present this problem in a way that is accessible to members of the community, and provide a dataset website (vlbiimaging.csail.mit.edu) that facilitates controlled comparisons across algorithms.
What strikes me as really amazing is the cross functional nature of these modern achievements. I did not realize that this image was created with statistical image models and a Bayesian approach.
Also, this link included -> http://vlbiimaging.csail.mit.edu/ introduces the field and offers a good explanation for those interested in learning more:
> Imaging distant celestial sources with high resolving power requires telescopes with prohibitively large diameters due to the inverse relationship between angular resolution and telescope diameter. However, by simultaneously collecting data from an array of telescopes located around the Earth, it is possible to emulate samples from a single telescope with a diameter equal to the maximum distance between telescopes in the array. Using multiple telescopes in this manner is referred to as very long baseline interferometry (VLBI).
No. Angular resolution is essentially the angular distance between two points that are still resolved as separate points. So if your resolution increases, angular resolution decreases, because you can resolve two points that are closer together.
Thanks. I read the Wiki on the matter; should have gone straight there instead of asking. After understanding what it is, angular resolution does make perfect sense a term, but at first glance was certainly a bit counterintuitive.
I think the reason it's confusing is that the way that bit in the article is worded does little to imply that you want a LOW angular resolution, and it doesn't directly mention resolution in and of itself (which is understood to have an inverse relationship with angular resolution, as it is directly affected by diameter).
It took me several rereads and reading the comments here to understand that we want low numbers for angular resolution.
I suppose it's fairly obvious for one well-versed in optics, but to the layman (like me) it's initially opaque.
I'm not sure, but from a class I'm taking right now, I have a faint inkling that the lesser light you let in, the more resolution you have i.e the more you're able to distinguish between two close together objects.
> Very long baseline interferometry (VLBI) is a technique for imaging celestial radio emissions by simultaneously observing a source from telescopes distributed across Earth. The challenges in reconstructing images from fine angular resolution VLBI data are immense. The data is extremely sparse and noisy, thus requiring statistical image models such as those designed in the computer vision community. In this paper we present a novel Bayesian approach for VLBI image reconstruction. While other methods often require careful tuning and parameter selection for different types of data, our method (CHIRP) produces good results under different settings such as low SNR or extended emission. The success of our method is demonstrated on realistic synthetic experiments as well as publicly available real data. We present this problem in a way that is accessible to members of the community, and provide a dataset website (vlbiimaging.csail.mit.edu) that facilitates controlled comparisons across algorithms.
What strikes me as really amazing is the cross functional nature of these modern achievements. I did not realize that this image was created with statistical image models and a Bayesian approach.
Also, this link included -> http://vlbiimaging.csail.mit.edu/ introduces the field and offers a good explanation for those interested in learning more:
> Imaging distant celestial sources with high resolving power requires telescopes with prohibitively large diameters due to the inverse relationship between angular resolution and telescope diameter. However, by simultaneously collecting data from an array of telescopes located around the Earth, it is possible to emulate samples from a single telescope with a diameter equal to the maximum distance between telescopes in the array. Using multiple telescopes in this manner is referred to as very long baseline interferometry (VLBI).