Consistent joint photometric and geometric image registration

In this paper, we derive a novel robust image alignment technique that performs joint geometric and photometric registration in the total least square sense. The main idea is to use the total least square metrics instead of the ordinary least square metrics, which is commonly used in the literature. While the OLS model indicates that the target image may contain noise and the reference image should be noise-free, this puts a severe limitation on practical registration problems. By introducing the TLS model, which allows perturbations in both images, we can obtain mutually consistent parameters. Experimental results show that our method is indeed much more consistent and accurate in presence of noise compared to existing registration algorithms

Lensfree computational microscopy tools for cell and tissue imaging at the point-of-care and in low-resource settings.

The recent revolution in digital technologies and information processing methods present important opportunities to transform the way optical imaging is performed, particularly toward improving the throughput of microscopes while at the same time reducing their relative cost and complexity. Lensfree computational microscopy is rapidly emerging toward this end, and by discarding lenses and other bulky optical components of conventional imaging systems, and relying on digital computation instead, it can achieve both reflection and transmission mode microscopy over a large field-of-view within compact, cost-effective and mechanically robust architectures. Such high throughput and miniaturized imaging devices can provide a complementary toolset for telemedicine applications and point-of-care diagnostics by facilitating complex and critical tasks such as cytometry and microscopic analysis of e.g., blood smears, Pap tests and tissue samples. In this article, the basics of these lensfree microscopy modalities will be reviewed, and their clinically relevant applications will be discussed

X-ray and Radio Variability of M31*, The Andromeda Galaxy Nuclear Supermassive Black Hole

We confirm our earlier tentative detection of M31* in X-rays and measure its light-curve and spectrum. Observations in 2004-2005 find M31* rather quiescent in the X-ray and radio. However, X-ray observations in 2006-2007 and radio observations in 2002 show M31* to be highly variable at times. A separate variable X-ray source is found near P1, the brighter of the two optical nuclei. The apparent angular Bondi radius of M31* is the largest of any black hole, and large enough to be well resolved with Chandra. The diffuse emission within this Bondi radius is found to have an X-ray temperature ~0.3 keV and density 0.1 cm-3, indistinguishable from the hot gas in the surrounding regions of the bulge given the statistics allowed by the current observations. The X-ray source at the location of M31* is consistent with a point source and a power law spectrum with energy slope 0.9+/-0.2. Our identification of this X-ray source with M31* is based solely on positional coincidence.Comment: 25 pages, 8 figures, submitted to Ap

Statistical performance analysis of a fast super-resolution technique using noisy translations

It is well known that the registration process is a key step for super-resolution reconstruction. In this work, we propose to use a piezoelectric system that is easily adaptable on all microscopes and telescopes for controlling accurately their motion (down to nanometers) and therefore acquiring multiple images of the same scene at different controlled positions. Then a fast super-resolution algorithm \cite{eh01} can be used for efficient super-resolution reconstruction. In this case, the optimal use of $r^2$ images for a resolution enhancement factor $r$ is generally not enough to obtain satisfying results due to the random inaccuracy of the positioning system. Thus we propose to take several images around each reference position. We study the error produced by the super-resolution algorithm due to spatial uncertainty as a function of the number of images per position. We obtain a lower bound on the number of images that is necessary to ensure a given error upper bound with probability higher than some desired confidence level.Comment: 15 pages, submitte