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Speeding Up Image Reconstruction Methods in Coded Mask γ Cameras Using Neural Networks: Application to the EM Algorithm

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Abstract

When using γ-ray coded-mask cameras, one does not get a direct image as in classical optical cameras but the correlation of the mask response with the source. Therefore the data must be mathematically treated in order to reconstruct the original sky sources. Generally this reconstruction is based on linear methods, such as correlating the detector plane with a reconstruction array, or non-linear ones such as iterative or maximization methods (i.e. the EM algorithm). The latter have a better performance but they increase the computational complexity by taking a lot of time to reconstruct an image. Here we present a method for speeding up such kind of algorithms by making use of a neural network with a back-propagation learning rule.

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Authors and Affiliations

  1. Centro de Astrobiología, Instituto Nacional de Técnica Aeroespacial, Ctra. de Ajalvir km 4, Torrejón de Ardoz, 28850, Madrid, Spain

    Fernando J. Ballesteros & Bartolo Luque

  2. Centro de Astrobiología, Instituto Nacional de Técnica Aeroespacial, Ctra. de Ajalvir km 4, Torrejón de Ardoz, 28850, Madrid, Spain

    Enrique M. Muro

Authors
  1. Fernando J. Ballesteros
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  2. Enrique M. Muro
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  3. Bartolo Luque
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Correspondence to Enrique M. Muro.

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Ballesteros, F.J., Muro, E.M. & Luque, B. Speeding Up Image Reconstruction Methods in Coded Mask γ Cameras Using Neural Networks: Application to the EM Algorithm. Experimental Astronomy 11, 207–222 (2001). https://doi.org/10.1023/A:1013101111446

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