Sunday, June 3, 2012
Many imaging applications require increasingly bright illumination sources, motivating the replacement of conventional thermal light sources with bright light-emitting diodes, superluminescent diodes and lasers. Despite their brightness, lasers and superluminescent diodes are poorly suited for full-field imaging applications because their high spatial coherence leads to coherent artefacts such as speckle that corrupt image formation. We recently demonstrated that random lasers can be engineered to provide low spatial coherence. Here, we exploit the low spatial coherence of specifically designed random lasers to demonstrate speckle-free full-field imaging in the setting of intense optical scattering. We quantitatively show that images generated with random laser illumination exhibit superior quality than images generated with spatially coherent illumination. By providing intense laser illumination without the drawback of coherent artefacts, random lasers are well suited for a host of full-field imaging applications from full-field microscopy to digital light projector systems.
To read the paper: http://www.nature.com/nphoton/journal/v6/n6/full/nphoton.2012.90.html
The dogma of signal processing maintains that a signal must be sampled at a rate at least twice its highest frequency in order to be represented without error. However, in practice, we often compress the data soon after sensing, trading off signal representation complexity (bits) for some error (consider JPEG image compression in digital cameras, for example). Clearly, this is wasteful of valuable sensing resources. Over the past few years, a new theory of "compressive sensing" has begun to emerge, in which the signal is sampled (and simultaneously compressed) at a greatly reduced rate.
As the compressive sensing research community continues to expand rapidly, it behooves us to heed Shannon's advice.
Compressive sensing is also referred to in the literature by the terms: compressed sensing, compressive sampling, and sketching/heavy-hitters.
The Compressive Sensing Resources:
Rice University Resources: http://dsp.rice.edu/cs
Nuit Blanche Blog: http://nuit-blanche.blogspot.com/
Compressive Sensing Research Groups:The Rice group led by Richard Baraniuk has been the leader in spearheading information diffusion on the subject of compressive sensing through theirRice Compressive Sensing Resource page. They also have a nice presentation of the now famous Rice Single pixel camera.
Terry Tao has made a list of the different matrices and their properties wrt compressive sensing in this page: Preprints in sparse recovery / Summary of properties of random matrices.
Feature Selection in Face Recognition: A Sparse Representation Perspectiveled by Allen Yang at Berkeley.
Duke DISP lab led by David Brady. Of particular interest is Ashwin Wagadarikar's page on the compressive sensing hyperspectral imager.
Gabriel Peyre, Chambolle's algorithm for the resolution of compressed sensing with TV regularization.
In super-resolution microscopy methods based on single-molecule switching, the rate of accumulating single-molecule activation events often limits the time resolution. Here we developed a sparse-signal recovery technique using compressed sensing to analyze images with highly overlapping fluorescent spots. This method allows an activated fluorophore density an order of magnitude higher than what conventional single-molecule fitting methods can handle. Using this method, we demonstrated imaging microtubule dynamics in living cells with a time resolution of 3 s.
This work was published in Nature Methods.