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Reconstructing Images in Astrophysics, an Inverse Problem Point of View

  • Céline TheysEmail author
  • Claude Aime
Chapter
Part of the Lecture Notes in Physics book series (LNP, volume 914)

Abstract

After a short introduction, a first section provides a brief tutorial to the physics of image formation and its detection in the presence of noises. The rest of the chapter focuses on the resolution of the inverse problem . In the general form, the observed image is given by a Fredholm integral containing the object and the response of the instrument. Its inversion is formulated using a linear algebra. The discretized object and image of size N × N are stored in vectors x and y of length N2. They are related one another by the linear relation y = Hx, where H is a matrix of size N2 × N2 that contains the elements of the instrument response. This matrix presents particular properties for a shift invariant point spread function for which the Fredholm integral is reduced to a convolution relation. The presence of noise complicates the resolution of the problem. It is shown that minimum variance unbiased solutions fail to give good results because H is badly conditioned, leading to the need of a regularized solution. Relative strength of regularization versus fidelity to the data is discussed and briefly illustrated on an example using L-curves. The origins and construction of iterative algorithms are explained, and illustrations are given for the algorithms ISRA , for a Gaussian additive noise, and Richardson–Lucy , for a pure photodetected image (Poisson statistics). In this latter case, the way the algorithm modifies the spatial frequencies of the reconstructed image is illustrated for a diluted array of apertures in space. Throughout the chapter, the inverse problem is formulated in matrix form for the general case of the Fredholm integral, while numerical illustrations are limited to the deconvolution case, allowing the use of discrete Fourier transforms, because of computer limitations.

Keywords

Discrete Fourier Transform Point Spread Function Regularization Term Karush Kuhn Tucker Reconstructed Object 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

Notes

Acknowledgements

We are grateful to organizers of the Besançon school for their invitation and to H. Lantéri for very fruitful discussions.

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Copyright information

© Springer International Publishing Switzerland 2016

Authors and Affiliations

  1. 1.UMR 7293 J.L. Lagrange, Observatoire de la Côte d’AzurUniversité de Nice Sophia AntipolisNice CedexFrance

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