Point Source Extraction with MOPEX

MOPEX (MOsaicking and Point source EXtraction) is a package developed at the Spitzer Science Center for astronomical image processing. We report on the point source extraction capabilities of MOPEX. Point source extraction is implemented as a two step process: point source detection and profile fitting. Non-linear matched filtering of input images can be performed optionally to increase the signal-to-noise ratio and improve detection of faint point sources. Point Response Function (PRF) fitting of point sources produces the final point source list which includes the fluxes and improved positions of the point sources, along with other parameters characterizing the fit. Passive and active deblending allows for successful fitting of confused point sources. Aperture photometry can also be computed for every extracted point source for an unlimited number of aperture sizes. PRF is estimated directly from the input images. Implementation of efficient methods of background and noise estimation, and modified Simplex algorithm contribute to the computational efficiency of MOPEX. The package is implemented as a loosely connected set of perl scripts, where each script runs a number of modules written in C/C++. Input parameter setting is done through namelists, ASCII configuration files. We present applications of point source extraction to the mosaic images taken at 24 and 70 micron with the Multiband Imaging Photometer (MIPS) as part of the Spitzer extragalactic First Look Survey and to a Digital Sky Survey image. Completeness and reliability of point source extraction is computed using simulated data.Comment: 20 pages, 13 Postscript figures, accepted for publication in PAS

Large-scale wave-front reconstruction for adaptive optics systems by use of a recursive filtering algorithm

We propose a new recursive filtering algorithm for wave-front reconstruction in a large-scale adaptive optics system. An embedding step is used in this recursive filtering algorithm to permit fast methods to be used for wave-front reconstruction on an annular aperture. This embedding step can be used alone with a direct residual error updating procedure or used with the preconditioned conjugate-gradient method as a preconditioning step. We derive the Hudgin and Fried filters for spectral-domain filtering, using the eigenvalue decomposition method. Using Monte Carlo simulations, we compare the performance of discrete Fourier transform domain filtering, discrete cosine transform domain filtering, multigrid, and alternative-direction-implicit methods in the embedding step of the recursive filtering algorithm. We also simulate the performance of this recursive filtering in a closed-loop adaptive optics system

A Convex Model for Edge-Histogram Specification with Applications to Edge-preserving Smoothing

The goal of edge-histogram specification is to find an image whose edge image has a histogram that matches a given edge-histogram as much as possible. Mignotte has proposed a non-convex model for the problem [M. Mignotte. An energy-based model for the image edge-histogram specification problem. IEEE Transactions on Image Processing, 21(1):379--386, 2012]. In his work, edge magnitudes of an input image are first modified by histogram specification to match the given edge-histogram. Then, a non-convex model is minimized to find an output image whose edge-histogram matches the modified edge-histogram. The non-convexity of the model hinders the computations and the inclusion of useful constraints such as the dynamic range constraint. In this paper, instead of considering edge magnitudes, we directly consider the image gradients and propose a convex model based on them. Furthermore, we include additional constraints in our model based on different applications. The convexity of our model allows us to compute the output image efficiently using either Alternating Direction Method of Multipliers or Fast Iterative Shrinkage-Thresholding Algorithm. We consider several applications in edge-preserving smoothing including image abstraction, edge extraction, details exaggeration, and documents scan-through removal. Numerical results are given to illustrate that our method successfully produces decent results efficiently