5 ms·

Seems really interesting, but I don't understand why you'd get rid of 90% of the image -- wouldn't it be much more useful to simply enhance the current images t

Seems really interesting, but I don't understand why you'd get rid of 90% of the image -- wouldn't it be much more useful to simply enhance the current images to 10x higher resolution? That seems to be what the scientists are doing for the MRI, but the author seems to think this would be useful in that it would allow people to throw away a lot of data. I think it would be much better for extrapolating even farther from the data we have.

jey 17y ago
It's more that you only need to collect 10% of the data to begin with, not necessarily throw away 90% of what you do have.
- oostevo 17y ago
  It turns out that the gains from only computing the paths of a subset of the rays are offset by the cost of recovering the compressively sensed data. I could be wrong, though -- I only did this back-of-the-envelope a few months ago. I never really tried that hard to make it work.
  - jey 17y ago
    (I think you meant to reply to a different comment.)
  - nzmsv 17y ago
    (I'm assuming this is a reply to my raytracing comment) This is most likely true. Additionally, raytracing just might produce samples that are too regular for this to work well. I haven't really thought about it :)
    - jey 17y ago
      Regularity helps, not hinders, but it's surely not going to be real-time. It might be faster than ray tracing every pixel, but that depends on many factors that I don't know/understand.
nzmsv 17y ago
Here's a blog post by Terrence Tao on the subject: http://terrytao.wordpress.com/2007/04/13/compressed-sensing-and-single-pixel-cameras/ http://terrytao.wordpress.com/2007/04/13/compressed-sensing-... One application he mentions is sensor networks that can withstand a loss of a large number of nodes. As soon as I saw the article my first thought was: "real-time raytracing?"
aphyr 17y ago
In this case, you don't "get rid" of 90% of the image--you simply never acquire it in the first place. The reason is that MRI sampling is prohibitively time-expensive: you need to keep the sample still for a long time to sample many points. In the article's case, that would require the patient to end up dead. ;) Sparsely sampling the volume takes much less time and allows for a reasonable reconstruction. I'm not intimately familiar with MRI processing, but there may also be some intricacy of the imaging process (possibly related to the time-to-spatial-frequency conversion?) that makes the lower-res sampling especially suited for this algorithm.

jey 17y ago

It's more that you only need to collect 10% of the data to begin with, not necessarily throw away 90% of what you do have.

oostevo 17y ago
It turns out that the gains from only computing the paths of a subset of the rays are offset by the cost of recovering the compressively sensed data. I could be wrong, though -- I only did this back-of-the-envelope a few months ago. I never really tried that hard to make it work.
- jey 17y ago
  (I think you meant to reply to a different comment.)
- nzmsv 17y ago
  (I'm assuming this is a reply to my raytracing comment) This is most likely true. Additionally, raytracing just might produce samples that are too regular for this to work well. I haven't really thought about it :)
  - jey 17y ago
    Regularity helps, not hinders, but it's surely not going to be real-time. It might be faster than ray tracing every pixel, but that depends on many factors that I don't know/understand.

nzmsv 17y ago

Here's a blog post by Terrence Tao on the subject: http://terrytao.wordpress.com/2007/04/13/compressed-sensing-and-single-pixel-cameras/ http://terrytao.wordpress.com/2007/04/13/compressed-sensing-... One application he mentions is sensor networks that can withstand a loss of a large number of nodes. As soon as I saw the article my first thought was: "real-time raytracing?"

aphyr 17y ago

In this case, you don't "get rid" of 90% of the image--you simply never acquire it in the first place. The reason is that MRI sampling is prohibitively time-expensive: you need to keep the sample still for a long time to sample many points. In the article's case, that would require the patient to end up dead. ;) Sparsely sampling the volume takes much less time and allows for a reasonable reconstruction. I'm not intimately familiar with MRI processing, but there may also be some intricacy of the imaging process (possibly related to the time-to-spatial-frequency conversion?) that makes the lower-res sampling especially suited for this algorithm.