Equivariant Imaging: Learning Beyond the Range Space

Dongdong Chen, Julián Tachella, Mike E. Davies

Research output: Chapter in Book/Report/Conference proceedingConference contribution

28 Citations (Scopus)


In various imaging problems, we 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. We propose a new end-to-end self-supervised framework 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 on real clinical data and image inpainting on natural images. Code has been made available at: https://github. com/edongdongchen/EI.
Original languageEnglish
Title of host publication2021 IEEE/CVF International Conference on Computer Vision (ICCV)
Number of pages10
ISBN (Electronic)9781665428125
Publication statusPublished - 28 Feb 2022
Event18th IEEE/CVF International Conference on Computer Vision 2021 - Virtual, Online, Canada
Duration: 11 Oct 202117 Oct 2021


Conference18th IEEE/CVF International Conference on Computer Vision 2021
Abbreviated titleICCV 2021
CityVirtual, Online


Dive into the research topics of 'Equivariant Imaging: Learning Beyond the Range Space'. Together they form a unique fingerprint.

Cite this