Abstract

Deep networks provide state-of-the-art performance in multiple imaging inverse problems ranging from medical imaging to computational photography. In various imaging problems, we usually only have access to compressed measurements of the underlying signals, hindering most learning-based strategies which usually require pairs of signals and associated measurements for training. Learning only from compressed measurements is impossible in general, as the compressed observations do not contain information outside the range of the forward sensing operator. In this talk, I will introduce a new end-to-end self-supervised framework, called Equivariant Imaging (EI) that overcomes this limitation by exploiting the equivariances present in natural signals. Our proposed learning strategy performs as well as fully supervised methods. Experiments demonstrate the potential of this framework on inverse problems including sparse-view X-ray computed tomography, accelerated MRI , and image inpainting.

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