Astronomical image reconstruction with convolutional neural networks

TL;DR

This paper explores using convolutional neural networks for astronomical image reconstruction, demonstrating that they offer a computationally efficient and competitive alternative to traditional optimization-based methods, with added interpretability.

Contribution

The paper introduces a CNN-based approach for astronomical image reconstruction that reduces computational complexity and maintains competitive performance.

Findings

CNN approach is computationally efficient with linear complexity per pixel.

The method achieves performance comparable to state-of-the-art techniques.

The approach is interpretable and suitable for large-scale astronomical data.

Abstract

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.