Multi-View Networks for Denoising of Arbitrary Numbers of Channels
This addresses a domain-specific problem in signal processing or imaging where flexible channel handling is needed, offering an incremental improvement over existing methods.
The paper tackles the problem of denoising with an arbitrary number of channels by proposing multi-view networks, which outperform traditional models in multi-channel scenarios and generalize to unseen channel counts.
We propose a set of denoising neural networks capable of operating on an arbitrary number of channels at runtime, irrespective of how many channels they were trained on. We coin the proposed models multi-view networks since they operate using multiple views of the same data. We explore two such architectures and show how they outperform traditional denoising models in multi-channel scenarios. Additionally, we demonstrate how multi-view networks can leverage information provided by additional recordings to make better predictions, and how they are able to generalize to a number of recordings not seen in training.