Consensus Convolutional Sparse Coding

Biswarup Choudhury, Robin Swanson, Felix Heide, Gordon Wetzstein, Wolfgang Heidrich

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

15 Scopus citations

Abstract

Convolutional sparse coding (CSC) is a promising direction for unsupervised learning in computer vision. In contrast to recent supervised methods, CSC allows for convolutional image representations to be learned that are equally useful for high-level vision tasks and low-level image reconstruction and can be applied to a wide range of tasks without problem-specific retraining. Due to their extreme memory requirements, however, existing CSC solvers have so far been limited to low-dimensional problems and datasets using a handful of low-resolution example images at a time. In this paper, we propose a new approach to solving CSC as a consensus optimization problem, which lifts these limitations. By learning CSC features from large-scale image datasets for the first time, we achieve significant quality improvements in a number of imaging tasks. Moreover, the proposed method enables new applications in high-dimensional feature learning that has been intractable using existing CSC methods. This is demonstrated for a variety of reconstruction problems across diverse problem domains, including 3D multispectral demosaicing and 4D light field view synthesis.

Original languageEnglish (US)
Title of host publicationProceedings - 2017 IEEE International Conference on Computer Vision, ICCV 2017
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages4290-4298
Number of pages9
ISBN (Electronic)9781538610329
DOIs
StatePublished - Dec 22 2017
Externally publishedYes
Event16th IEEE International Conference on Computer Vision, ICCV 2017 - Venice, Italy
Duration: Oct 22 2017Oct 29 2017

Publication series

NameProceedings of the IEEE International Conference on Computer Vision
Volume2017-October
ISSN (Print)1550-5499

Other

Other16th IEEE International Conference on Computer Vision, ICCV 2017
CountryItaly
CityVenice
Period10/22/1710/29/17

All Science Journal Classification (ASJC) codes

  • Software
  • Computer Vision and Pattern Recognition

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    Choudhury, B., Swanson, R., Heide, F., Wetzstein, G., & Heidrich, W. (2017). Consensus Convolutional Sparse Coding. In Proceedings - 2017 IEEE International Conference on Computer Vision, ICCV 2017 (pp. 4290-4298). [8237721] (Proceedings of the IEEE International Conference on Computer Vision; Vol. 2017-October). Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/ICCV.2017.459