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ADN
Authors:
Abstract: ADN permits to add photon noise, background noise, read-out noise and dark-current noise to an image or a cube of images. ADN allows also to simulate the saturation of the CCD as well as the perturbations due to flat-field and bad-pixels.

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Posted by: admin, on 3/25/2007, in category "Modules of the Software Package AIRY "
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ANB
Authors:
Abstract: ANB module performs the astrometric and photometric analysis on a restored binary type object.

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Posted by: admin, on 3/25/2007, in category "Modules of the Software Package AIRY "
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CBD
Authors:
Abstract: CBD performs the deconvolution of a cube of images (i.e. p images corresponding to different orientations of the baseline) when the user don't have a PSF. The module gives back an image and an estimate of the PSF(s) and has in input a PSF(s) and an image(s). If the user has already an initial estimate of the PSF(s) he can give it directly in input; otherwise the module generates an estimate by an operation based on the autocorrelation operator on the image supplied in input. It is possible to obtain these two reconstructions by perform alternatively two partial reconstructions, the first one to the image(s) and the second one to the PSF(s). There are two different methods for image deconvolution and just one for PSFs reconstruction. In the first case the user can choose between the OS-EM method (see for details: "Application of the OS-EM method to the restoration of LBT images", M. Bertero and P. Boccacci, A&AS 144, May 2000, 181-186) and the LR-EM method. CBD fits the PSFs dimensions to the images dimensions, if they do not match, through zero-padding. The background evaluation performed by PRE module is used to restore the object with a correct sky-value, while the reconstructed PSFs are managed without background (this is subtracted before the deconvolution process). In this version no stopping rule is given. The rates of convergence may be different for different types of objects. This algorithm is sensible to the number of the iterations that the user set in the reconstruction box of images and PSFs. In the case of a point-like stellar object, it is easy to have a good estimate of the initial PSFs. In the case of diffuse objects (i.e. galaxies) is more difficult to obtain a good estimate about the PSFs shape making the autocorrelation of the input image and it is recommended to supply a different one.

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Posted by: admin, on 3/25/2007, in category "Modules of the Software Package AIRY "
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CNV
Authors:
Abstract: CNV permits to make a convolution between a 2D (3D) PSFs and an object 2D (3D) map. The parameters related to the observing conditions (efficiency, integration time, telescope surface collecting surface) can be chosen. The pixel size of the resulting cube of images is driven by the PSFs pixel size, whatever is the object map one. Both the object map and the PSFs arrays are by the way put in 2^N (N integer) pixels arrays, if they are not originally 2^N arrays. The maximum size for this last operation is set to 2048 pixels (that corresponds to N=11).

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Posted by: admin, on 3/25/2007, in category "Modules of the Software Package AIRY "
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Authors:
Abstract: DEC performs the deconvolution of a cube of images (i.e. p images corresponding to different orientations of the baseline). There are four different methods for image deconvolution, according to the noise model. For Poisson noise, the Richardson-Lucy (RL) and the Ordered-Subset Expectation Maximization (OSEM) methods are available, while for Gaussian noise both the Image Space Reconstruction Algorithm (ISRA) and the OS-ISRA are implemented. (see [1] for details). DEC also allows to reduce the boundary effects in the reconstructed image (see [2] for details). Accelerated versions of the algorithms are also available, following the Biggs and Andrews method (see [3,4] for details). When the object is complex, like a high dynamic object, a regularized version of the algorithm can give better results. In this cases, five different regularizations are available in DEC: Tikhonov, Laplacian, Entropy, Edge Preserving, and High Dynamic Range (see [1] for details). Depending on the regularization chosen, one or a couple of parameters control the efficacy of the regularization. The background evaluation performed by PRE module is used to restore the object with a correct sky-value. In this new version of DEC up to four different stopping rules are given. Concerning both RL and OSEM algorithms, four stopping rules are implemented: 1) Set a total number of iterations and stop the algorithm when this number is reached. This is valid for all available methods; 2) Stop the iteration when the discrepancy function crosses 1. This criterion is called discrepancy principle for Poisson data (see [5]) and can be used when no regularization has been used. 3) Stop the iterations when the total functional is approximately constant, according to a user-defined tolerance. This stopping rule can be applied only when a regularization is chosen. 4) Stop the iterations when the relative r.m.s error reaches a minimum value. This stopping rule can be used only in the case of numerical simulations, when the true object is known. The first and the last stopping rules are also available for ISRA and OS-ISRA. In DEC we also implemented an algorithm for super-resolving compact objects such as a binary system with an angular separation smaller than the angular resolution of the telescope. (see [6] for details). The method is based on a simple modification of the RL/OSEM method and in general consist of 3 steps: the first one requires a large number of RL/OSEM iterations (typically 10000), which are used to estimate the domain of the unresolved object; the second one is a RL/OSEM restoration (typically 5000 iterations) initialized with the mask of the domain, as estimated in step 1. These two steps are used to estimate the positions of the two stars while their magnitudes can be obtained in a possible 3rd step by solving a simple least-squares problem. The first two steps are included in DEC. In the second step it is possible to choose, in the GUI, the image and the mask used to initialized the method. The mask is an image with values 0/1. There are 3 kind of masks: the 1st is a mask based on percentage of the image maximum, the 2nd one is a circular mask, and the 3rd is a user-defined mask. References: [1] La Camera et al., 2012, "AIRY: a complete tool for the simulation and the reconstruction of astronomical images", Proc. SPIE, toappear [2] Anconelli et al., 2006, "Reduction of boundary effects in multiple image deconvolution with an application to LBT LINC-NIRVANA", A&A 448, 1217–1224. [3] Biggs and Andrews, 1997, "Acceleration of iterative image restoration algorithms", Applied Optics 36, 1766. [4] B

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Posted by: admin, on 3/25/2007, in category "Modules of the Software Package AIRY "
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DSP
Authors:
Abstract: DSP executes the simulation for the data DiSPlay (DSP) module. It can display the relevant field of each defined output type of the Software Package AIRY, i.e.: -source/object type (src_t or obj_t): displays the 2D-map. -image type (img_t): displays either a single 2D image or a series of 2D images in several display windows.

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Posted by: admin, on 3/25/2007, in category "Modules of the Software Package AIRY "
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FSM
Authors:
Abstract: FSM module find stars and perform the astrometric and photometric analysis, given a detection threshold expressed in RMS background (like in PRE module the MMM algorithm is used for background evaluation, unless it fails in which case the border of the frame is used to compute this value). A final display, in square-root scale, is given with the found stars encircled, as well as the listing of the stars location and aperture photometry.

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Posted by: admin, on 3/25/2007, in category "Modules of the Software Package AIRY "
Views: this article has been read 66686 times
OBJ
Authors:
Abstract: OBJ permits to define map of different types of astronomical objects as a target for observation and/or subsequent image reconstruction/deconvolution. Current object types are: single star, binary object, open cluster, planetary nebulae, supernovae remnant, spiral galaxy, Young Stellar Objects (YSOs), stellar surface and user-defined. For each object type the map is defined by its dimension [npixel] and pixel size. In particular, some remarks concerning the definition of some objects are the following : Single star : pixel located at [npixel/2,npixel/2] containing all the flux (i.e. unresolved star or point source). One may pay attention to the fact that such a definition will creates artifacts if one use PSFs (for both the convolution/ modelization and deconvolution) which are centered on [npixel-1/2,npixel-1/2]. A work around is to use a dirac map (pixel at [0,0] set to 1, 0 elsewhere) within the user-defined object type. Binary object : Formed by two point-source stars for which the barycenter (not photocenter) is located in [npixel/2,npixel/2]. In order to center each component on a pixel, the separation and position angle may vary slightly from the one set by user in the GUI. A warning appears if such a variation is more than 1%.

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Posted by: admin, on 3/25/2007, in category "Modules of the Software Package AIRY "
Views: this article has been read 5708 times
PEX
Authors:
Abstract: PEX performs PSF extrapolation from a cube of images (or from a single image). The extraction can be done either when project is "set up" (when parameters of the modules are chosen and saved) or when project runs. This operation is divided between PEX_OPEN_GUI and PEX_INIT procedures.
PEX_OPEN_GUI: users can load the image, set up scale, zoom, etc., then they can select one or more star with a simple point&click operation, and also select a suitable 'radius of extraction' (R) and the value of the beta parameter. Users have to complete the information about PSF (pixel-size, band, etc.). PEX_INIT: PSF is extracted by following steps:
For each selected star,
  - centroid of the star is computed and its position is shifted to the center of the image with a sub-pixel precision;
  - a domain of size 2R x 2R is extracted from the central region (this image will be denoted by H);
  - Moffat extrapolation of H(n) (where n=[n1,n2] is the multi-index characterizing the pixels of the image) is executed by following sub-steps:
   -- a first estimation of background is performed with a bilinear function: B(n) = a0 + a1*n1 + a2*n2
   -- the image H - a1*n1 - a2*n2 is fitted with a circular symmetric Moffat function:
                              b1
   M0(r) = b0 + -----------------------------              where r = sqrt(n1^2 + n2^2)
                    (1 + r^2 / b2^2)^beta
   -- the estimated values of the parameters ai and bi are used as initial guesses for a least-squares best-fitting of H(n) with a rotated elliptical Moffat function superimposed with a linear varying background:
                                                               c3
   M(n) = c0 + c1*n1 + c2*n2 + -------------------------------------------------
                                              (1 + (n1'/c4)^2 + (n2'/c5)^2)^beta
where n1'=n1*cos(q) + n2*sin(q) and n2'=-n1*sin(q) + n2*cos(q).
The value of b2 is the initial guess for both c4 and c5, while the initial guess of the rotation angle q in zero.
   -- the extracted image H(n) is merged into the sampled function M(n) defined on the full image and the linearly background is subtracted and the result is normalized to unit volume.
  If several stars are selected in the same image, the median of all the results of previous procedure is computed and this is normalized again to unit volume.

Rating: 194 user(s) have rated this article Average rating: 5.0
Posted by: andrea, on 7/5/2011, in category "Modules of the Software Package AIRY "
Views: this article has been read 5979 times
PRE
Authors:
Abstract: PRE permits to pre-process a cube of images before a subsequent image restoration/deconvolution process. More precisely, the module permits:
  • data reduction (also called data calibration),
  • shift-and-add procedure for multi-frame images,
  • positivization of the calibrated images,
  • sky background evaluation,
  • SNR evaluation,
  • image re-centering.

Rating: 986 user(s) have rated this article Average rating: 5.0
Posted by: admin, on 3/25/2007, in category "Modules of the Software Package AIRY "
Views: this article has been read 7549 times
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