Compressive Sensing Hardware Implementations This Week

Here is a compressive sensing hardware entry today. Thinking some more about yesterday's entry, the main crux of the issue is that the system presented has too sparse of a PSF. Anyway, I found it very refreshing the ability for the authors of this paper to get some feedback thanks to a tweet. More on the discussion can be found here. If you haven't read this week-end's entries, let me remind you that Muthu and I will be having a video Hangout on Google+ this Wednesday and you are welcome to join.

In the meantime, last week saw two presentations on hardware related instantiation of compressive sensing:

• A few applications of Compressed Sensing in acoustics by Laurent Daudet
• Architectures for Compressed Sensing and Sampling Correlated Signals by Justin Romberg

as well as two papers that will soon be part of the compressive sensing hardware implementation page.

Compressive Sensing in Holography by Adrian Stern, Yair Rivenson. The abstract reads:

Compressive sensing provides a new framework for simultaneous sampling and compression of signals. According to compressive sensing theory one can recover compressible signals and images from far fewer samples or measurements that traditional methods use. Applying compressive sensing theory for holography comes natural since three-dimensional (3D) data is typically very redundant, thus it is also very compressible. Holographic imaging itself is inherently a compressive process that encodes 3D data in 2D images. The application of compressive sensing for holography may reduce the acquisition effort in terms of acquisition time and hardware; it reduces the overall captured data thus reducing storage requirements and transmission bandwidth. We present a survey of compressive sensing techniques proposed for coherent and incoherent holography. We also present theoretical results that provide applicative practical rules for using of compressive sensing theory in holography.

I have mentioned this paper before, but let me do so again:


Photon-counting compressive sensing laser radar for 3D imaging by Gregory Howland, Paul Dixon, and John Howell.. The abstract reads:

We experimentally demonstrate a photon-counting, single-pixel, laser radar camera for 3D imaging where transverse spatial resolution is obtained through compressive sensing without scanning. We use this technique to image through partially obscuring objects, such as camouflage netting. Our implementation improves upon pixel-array based designs with a compact, resource-efficient design and highly scalable resolution.

Liked this entry ? subscribe to Nuit Blanche's feed, there's more where that came from. You can also subscribe to Nuit Blanche by Email, explore the Big Picture in Compressive Sensing or the Matrix Factorization Jungle and join the conversations on compressive sensing, advanced matrix factorization and calibration issues on Linkedin.