Deconvolution with Correct Sampling
By Pierre Magain, F. Courbin, S. Institute, Belgium Observatoire de Meudon, France. 1997
A new method for improving the resolution of astronomical images is presented. It is based on the principle that sampled data cannot be fully deconvolved without violating the sampling theorem. Thus, the sampled image should be deconvolved not by the total point-spread function but by a narrower function chosen so that the resolution of the deconvolved image is compatible with the adopted sampling. Our deconvolution method gives results that are, in at least some cases, superior to those of other commonly used techniques: in particular, it does not produce ringing around point sources superposed on a smooth background. Moreover, it allows researchers to perform accurate astrometry and photometry of crowded fields. These improvements are a consequence of both the correct treatment of sampling and the recognition that the most probable astronomical image is not a flat one. The method is also well adapted to the optimal combination of different images of the same object, as can be obtained, e.g., from infrared observations or via adaptive optics techniques.
Subfields: Computer Vision and Pattern Recognition
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Citations in the corpus
- Generative Adversarial Networks recover features in astrophysical images of galaxies beyond the deconvolution limit (2017, background): “Deconvolution has long been known as an ‘ill-posed’ inverse problem because there is often no unique solution if one follows the signal processing approach of backwards modelling (Magain et al. 1998, 2007; Letawe et al. 2007, 2008; Cantale et al. 2016).”
- H0LiCOW - IV. Lens mass model of HE 0435-1223 and blind measurement of its time-delay distance for cosmology (2016, methodology): “The photometry of the four images of HE 0435−1223 is obtained on each exposure using the Magain et al. (1998) deconvolution photometry presented in Cantale et al. (2016b).”
- The ZipML Framework for Training Models with End-to-End Low Precision: The Cans, the Cannots, and a Little Bit of Deep Learning (2016, unknown): “Deconvolution has long been known as an “ill-posed” inverse problem because there is often no unique solution if one follows the signal processing approach of backwards modelling [32, 31, 25, 26, 8].”
- TWO ACCURATE TIME-DELAY DISTANCES FROM STRONG LENSING: IMPLICATIONS FOR COSMOLOGY (2012, methodology): “Resolved light curves of the four AGN images are extracted from these observations by “deconvolution photometry,” following Magain et al. (1998).”
- H0LiCOW - V. New COSMOGRAIL time delays of HE 0435-1223: H0 to 3.8 per cent precision from strong lensing in a flat ΛCDM model (2016, methodology): “(ii) The photometric measurements of the four blended images of HE 0435−1223 are obtained using deconvolution photometry using the MCS deconvolution algorithm (Magain et al. 1998; Cantale et al. 2016).”
- Deconvolution in Astronomy: A Review (2002, background): “Magain et al. (1998) proposed an innovative alternative to this problem by assuming that the PSF can be considered as the convolution product of two terms, the ICFG and an unknownS: .”
- H0LiCOW – I. H0 Lenses in COSMOGRAIL's wellspring: program overview (2016, methodology): “The MCS deconvolution method (Magain et al. 1998; Cantale et al. 2016) is used to extract the photometry of the quasar images for building the light curves.”
- Time delay cosmography (2016, methodology): “One of the keys to the success of this program has been the simultaneous deconvolution of the individual frames in the imaging dataset, using a mixture model to describe the point-like quasar images and extended lens and AGN host galaxies (Magain et al. 1998).”