Astronomical image reconstruction with convolutional neural networks

State of the art methods in astronomical image reconstruction rely on the resolution of a regularized or constrained optimization problem. Solving this problem can be computationally intensive and usually leads to a quadratic or at least superlinear complexity w.r.t. the number of pixels in the imag...

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Bibliographic Details
Published in2017 25th European Signal Processing Conference (EUSIPCO) pp. 2468 - 2472
Main Author Flamary, Remi
Format Conference Proceeding
LanguageEnglish
Published EURASIP 01.08.2017
Subjects
Complexity theory
Convolution
Image reconstruction
Neural networks
Optimization
Training
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Summary:State of the art methods in astronomical image reconstruction rely on the resolution of a regularized or constrained optimization problem. Solving this problem can be computationally intensive and usually leads to a quadratic or at least superlinear complexity w.r.t. the number of pixels in the image. We investigate in this work the use of convolutional neural networks for image reconstruction in astronomy. With neural networks, the computationally intensive tasks is the training step, but the prediction step has a fixed complexity per pixel, i.e. a linear complexity. Numerical experiments show that our approach is both computationally efficient and competitive with other state of the art methods in addition to being interpretable.
ISSN:2076-1465
DOI:10.23919/EUSIPCO.2017.8081654