Astronomical image reconstruction with convolutional neural networks
This addresses the computational bottleneck in astronomical image processing, offering a more efficient alternative to traditional optimization-based methods.
The paper tackled the problem of computationally intensive astronomical image reconstruction by using convolutional neural networks, resulting in a method that is computationally efficient with linear complexity per pixel and competitive with state-of-the-art methods.
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.