Deep, complex, invertible networks for inversion of transmission effects in multimode optical fibres
We use complex-weighted, deep networks to invert the effects of multimode optical fibre distortion of a coherent input image. We generated experimental data based on collections of optical fibre responses to greyscale input images generated with coherent light, by measuring only image amplitude (not amplitude and phase as is typical) at the output of 1 m and 10 m long, 105 µm diameter multimode fibre. This data is made available as the Optical fibre inverse problem Benchmark collection. The experimental data is used to train complex-weighted models with a range of regularisation approaches. A unitary regularisation approach for complexweighted networks is proposed which performs well in robustly inverting the fibre transmission matrix, which is compatible with the physical theory. A benefit of the unitary constraint is that it allows us to learn a forward unitary model and analytically invert it to solve the inverse problem. We demonstrate this approach, and outline how it has the potential to improve performance by incorporating knowledge of the phase shift induced by the spatial light modulator.
I have read this paper and found it to be a solid contribution both to the field of optics and to NIPS.  With respect to its contribution to optics, I will admit that I have not worked in this field for over two decades, but it does seem that they are solving an interesting problem in a new way.  The removal of speckle from images seems to be a relevant problem in the field:    (2018) SAR Image Despeckling Using a Convolutional Neural Network Puyang Wang, Student Member, IEEE, He Zhang, Student Member, IEEE and Vishal M. Patel, Senior Member, IEEE https://arxiv.org/pdf/1706.00552.pdf  Most cited (2010) "General Bayesian estimation for speckle noise reduction in optical coherence tomography retinal imagery" Alexander Wong, Akshaya Mishra, Kostadinka Bizheva, David A. Clausi1  And I could not find a similar work in my search.    The authors contribute compelling examples in their supplemental materials, which lends credence to the claims that their method actually works.  The authors also promise to contribute a novel dataset to use for ML benchmarking on this type of problem.    In my opinion, this paper comprises what I believe a NIPS application paper should have, a paper that would be considered best in class in it's field of application and one that uses neural information processing methods to bring a substantial advance to the application domain.  Additionally the contribution of a benchmark dataset should further motivate research on this problem area.  The paper is well written and easy to follow.