2016
DOI: 10.1051/0004-6361/201424003
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Firedec: a two-channel finite-resolution image deconvolution algorithm

Abstract: We present a two-channel deconvolution method that decomposes images into a parametric point-source channel and a pixelized extended-source channel. Based on the central idea of the deconvolution algorithm proposed by Magain et al. (1998, ApJ, 494, 472), the method aims at improving the resolution of the data rather than at completely removing the point spread function (PSF). Improvements over the original method include a better regularization of the pixel channel of the image, based on wavelet filtering and… Show more

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Cited by 38 publications
(44 citation statements)
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“…In practice, the shape of the target PSF is chosen to be a circular Gaussian. The description of the new algorithm is presented in Cantale et al (2016). The new features, which specifically benefited from the deconvolution of the EDisCS data, are an efficient regularization strategy, a new minimization, and a proper noise treatment.…”
Section: Image Deconvolutionmentioning
confidence: 99%
“…In practice, the shape of the target PSF is chosen to be a circular Gaussian. The description of the new algorithm is presented in Cantale et al (2016). The new features, which specifically benefited from the deconvolution of the EDisCS data, are an efficient regularization strategy, a new minimization, and a proper noise treatment.…”
Section: Image Deconvolutionmentioning
confidence: 99%
“…LSST will also have very precise photometric redshifts and good image quality and this technique should be possible for lower redshift samples where the ground-based seeing results in sufficient physical resolution. Finally deconvolution methods such as in Cantale et al (2016a) can be applied to ground-based imaging surveys making it possible to carry out analysis on these surveys to much larger distances.…”
Section: Discussionmentioning
confidence: 99%
“…We carried out the photometric measurements using the deconvolution photometry with the so-called MCS image deconvolution algorithm (Magain et al 1998;Cantale et al 2016). This algorithm first computes a deconvolution kernel from the images of stars.…”
Section: Observations and Photometrymentioning
confidence: 99%