MY NOTE: Wish I could say that I truly understood the mathematics behind compressive sensing, but I do understand the concept, as ably presented by Igor Carron on his blog Nuit Blanche, and I can also see how it will influence and make all scanning technologies better. (Igor has promised me a blog post here, and I look forward to it, when he has the time)
Here is a new compressive sensor: Compressive Fluorescence Microscopy for Biological and Hyperspectral Imaging by Vincent Studer, Jérome Bobin, Makhlad Chahid, Hamed Moussavi, Emmanuel Candes and Maxime Dahan. The abstract reads:
The mathematical theory of compressed sensing (CS) asserts that one can acquire signals from measurements whose rate is much lower than the total bandwidth. Whereas the CS theory is now well developed, challenges concerning hardware implementations of CS-based acquisition devices—especially in optics—have only started being addressed. This paper presents an implementation of compressive sensing in fluorescence microscopy and its applications to biomedical imaging. Our CS microscope combines a dynamic structured wide-field illumination and a fast and sensitive single-point fluorescence detection to enable reconstructions of images of fluorescent beads, cells and tissues with undersampling ratios (between the number of pixels and number of measurements) up to 32. We further demonstrate a hyperspectral mode and record images with 128 spectral channels and undersampling ratios up to 64, illustrating the potential benefits of CS acquisition for higher dimensional signals which typically exhibits extreme redundancy. Altogether, our results emphasize the interest of CS schemes for acquisition at a significantly reduced rate and point out to some remaining challenges for CS fluorescence microscopy
No comments:
Post a Comment