liu.seSearch for publications in DiVA
Change search
ReferencesLink to record
Permanent link

Direct link
Compressive Image Reconstruction in Reduced Union of Subspaces
Linköping University, Department of Science and Technology, Media and Information Technology. Linköping University, Faculty of Science & Engineering. (Computer Graphics and Image Processing)
Linköping University, Department of Science and Technology, Media and Information Technology. Linköping University, Faculty of Science & Engineering. (Computer Graphics and Image Processing)
Linköping University, Department of Science and Technology, Media and Information Technology. Linköping University, Faculty of Science & Engineering. (Computer Graphics and Image Processing)ORCID iD: 0000-0002-7765-1747
2015 (English)In: Computer Graphics Forum, ISSN 1467-8659, Vol. 34, no 2, 33-44 p.Article in journal (Refereed) Published
Abstract [en]

We present a new compressed sensing framework for reconstruction of incomplete and possibly noisy images and their higher dimensional variants, e.g. animations and light-fields. The algorithm relies on a learning-based basis representation. We train an ensemble of intrinsically two-dimensional (2D) dictionaries that operate locally on a set of 2D patches extracted from the input data. We show that one can convert the problem of 2D sparse signal recovery to an equivalent 1D form, enabling us to utilize a large family of sparse solvers. The proposed framework represents the input signals in a reduced union of subspaces model, while allowing sparsity in each subspace. Such a model leads to a much more sparse representation than widely used methods such as K-SVD. To evaluate our method, we apply it to three different scenarios where the signal dimensionality varies from 2D (images) to 3D (animations) and 4D (light-fields). We show that our method outperforms state-of-the-art algorithms in computer graphics and image processing literature.

Place, publisher, year, edition, pages
John Wiley & Sons Ltd , 2015. Vol. 34, no 2, 33-44 p.
Keyword [en]
Image reconstruction, compressed sensing, light field imaging
National Category
Signal Processing
Identifiers
URN: urn:nbn:se:liu:diva-119639DOI: 10.1111/cgf.12539ISI: 000358326600008OAI: oai:DiVA.org:liu-119639DiVA: diva2:825377
Conference
Eurographics 2015
Projects
VPS
Funder
Swedish Foundation for Strategic Research , IIS11-0081
Available from: 2015-06-23 Created: 2015-06-23 Last updated: 2015-09-22Bibliographically approved

Open Access in DiVA

No full text

Other links

Publisher's full textProject web page

Search in DiVA

By author/editor
Miandji, EhsanKronander, JoelUnger, Jonas
By organisation
Media and Information TechnologyFaculty of Science & Engineering
Signal Processing

Search outside of DiVA

GoogleGoogle Scholar
The number of downloads is the sum of all downloads of full texts. It may include eg previous versions that are now no longer available

Altmetric score

Total: 146 hits
ReferencesLink to record
Permanent link

Direct link