An Improved Observation Model for Super-Resolution under Affine Motion

Super-resolution (SR) techniques make use of subpixel shifts between frames in an image sequence to yield higher-resolution images. We propose an original observation model devoted to the case of non isometric inter-frame motion as required, for instance, in the context of airborne imaging sensors. First, we describe how the main observation models used in the SR literature deal with motion, and we explain why they are not suited for non isometric motion. Then, we propose an extension of the observation model by Elad and Feuer adapted to affine motion. This model is based on a decomposition of affine transforms into successive shear transforms, each one efficiently implemented by row-by-row or column-by-column 1-D affine transforms. We demonstrate on synthetic and real sequences that our observation model incorporated in a SR reconstruction technique leads to better results in the case of variable scale motions and it provides equivalent results in the case of isometric motions

Imaging a Quasar Accretion Disk with Microlensing

We show how analysis of a quasar high-magnification microlensing event may be used to construct a map of the frequency-dependent surface brightness of the quasar accretion disk. The same procedure also allows determination of the disk inclination angle, the black hole mass (modulo the caustic velocity), and possibly the black hole spin. This method depends on the validity of one assumption: that the optical and ultraviolet continuum of the quasar is produced on the surface of an azimuthally symmetric, flat equatorial disk, whose gas follows prograde circular orbits in a Kerr spacetime (and plunges inside the marginally stable orbit). Given this assumption, we advocate using a variant of first-order linear regularization to invert multi-frequency microlensing lightcurves to obtain the disk surface brightness as a function of radius and frequency. The other parameters can be found by minimizing chi-square in a fashion consistent with the regularized solution for the surface brightness. We present simulations for a disk model appropriate to the Einstein Cross quasar, an object uniquely well-suited to this approach. These simulations confirm that the surface brightness can be reconstructed quite well near its peak, and that there are no systematic errors in determining the other model parameters. We also discuss the observational requirements for successful implementation of this technique.Comment: accepted to ApJ for publicatio

Mammographic image restoration using maximum entropy deconvolution

An image restoration approach based on a Bayesian maximum entropy method (MEM) has been applied to a radiological image deconvolution problem, that of reduction of geometric blurring in magnification mammography. The aim of the work is to demonstrate an improvement in image spatial resolution in realistic noisy radiological images with no associated penalty in terms of reduction in the signal-to-noise ratio perceived by the observer. Images of the TORMAM mammographic image quality phantom were recorded using the standard magnification settings of 1.8 magnification/fine focus and also at 1.8 magnification/broad focus and 3.0 magnification/fine focus; the latter two arrangements would normally give rise to unacceptable geometric blurring. Measured point-spread functions were used in conjunction with the MEM image processing to de-blur these images. The results are presented as comparative images of phantom test features and as observer scores for the raw and processed images. Visualization of high resolution features and the total image scores for the test phantom were improved by the application of the MEM processing. It is argued that this successful demonstration of image de-blurring in noisy radiological images offers the possibility of weakening the link between focal spot size and geometric blurring in radiology, thus opening up new approaches to system optimization.Comment: 18 pages, 10 figure

Automated Lensing Learner: Automated Strong Lensing Identification with a Computer Vision Technique

Forthcoming surveys such as the Large Synoptic Survey Telescope (LSST) and Euclid necessitate automatic and efficient identification methods of strong lensing systems. We present a strong lensing identification approach that utilizes a feature extraction method from computer vision, the Histogram of Oriented Gradients (HOG), to capture edge patterns of arcs. We train a supervised classifier model on the HOG of mock strong galaxy-galaxy lens images similar to observations from the Hubble Space Telescope (HST) and LSST. We assess model performance with the area under the curve (AUC) of a Receiver Operating Characteristic (ROC) curve. Models trained on 10,000 lens and non-lens containing images images exhibit an AUC of 0.975 for an HST-like sample, 0.625 for one exposure of LSST, and 0.809 for 10-year mock LSST observations. Performance appears to continually improve with the training set size. Models trained on fewer images perform better in absence of the lens galaxy light. However, with larger training data sets, information from the lens galaxy actually improves model performance, indicating that HOG captures much of the morphological complexity of the arc finding problem. We test our classifier on data from the Sloan Lens ACS Survey and find that small scale image features reduces the efficiency of our trained model. However, these preliminary tests indicate that some parameterizations of HOG can compensate for differences between observed mock data. One example best-case parameterization results in an AUC of 0.6 in the F814 filter image with other parameterization results equivalent to random performance.Comment: 18 pages, 14 figures, summarizing results in figure