Mid Sweden University

miun.sePublications
Change search
CiteExportLink to record
Permanent link

Direct link
Cite
Citation style
  • apa
  • ieee
  • modern-language-association-8th-edition
  • vancouver
  • Other style
More styles
Language
  • de-DE
  • en-GB
  • en-US
  • fi-FI
  • nn-NO
  • nn-NB
  • sv-SE
  • Other locale
More languages
Output format
  • html
  • text
  • asciidoc
  • rtf
Multi-Mask Camera Model for Compressed Acquisition of Light Fields
2021 (English)In: IEEE Transactions on Computational Imaging, ISSN 2573-0436, E-ISSN 2333-9403, Vol. 7, p. 191-208, article id 9336269Article in journal (Refereed) Published
Abstract [en]

We present an all-in-one camera model that encompasses the architectures of most existing compressive-sensing light-field cameras, equipped with a single lens and multiple amplitude coded masks that can be placed at different positions between the lens and the sensor. The proposed model, named the equivalent multi-mask camera (EMMC) model, enables the comparison between different camera designs, e.g using monochrome or CFA-based sensors, single or multiple acquisitions, or varying pixel sizes, via a simple adaptation of the sampling operator. In particular, in the case of a camera equipped with a CFA-based sensor and a coded mask, this model allows us to jointly perform color demosaicing and light field spatio-angular reconstruction. In the case of variable pixel size, it allows to perform spatial super-resolution in addition to angular reconstruction. While the EMMC model is generic and can be used with any reconstruction algorithm, we validate the proposed model with a dictionary-based reconstruction algorithm and a regularization-based reconstruction algorithm using a 4D Total-Variation-based regularizer for light field data. Experimental results with different reconstruction algorithms show that the proposed model can flexibly adapt to various sensing schemes. They also show the advantage of using an in-built CFA sensor with respect to monochrome sensors classically used in the literature. © 2015 IEEE.

Place, publisher, year, edition, pages
Institute of Electrical and Electronics Engineers Inc. , 2021. Vol. 7, p. 191-208, article id 9336269
Keywords [en]
camera models, compressed sensing, inverse problems, Light Field imaging, regularization, Pixels, Camera design, Color demosaicing, Compressive sensing, Multiple acquisitions, Reconstruction algorithms, Sensing schemes, Super resolution, Total variation, Cameras
Identifiers
URN: urn:nbn:se:miun:diva-43453DOI: 10.1109/TCI.2021.3053702ISI: 000617750700002Scopus ID: 2-s2.0-85100505368OAI: oai:DiVA.org:miun-43453DiVA, id: diva2:1603732
Available from: 2021-10-18 Created: 2021-10-18 Last updated: 2021-10-18Bibliographically approved

Open Access in DiVA

No full text in DiVA

Other links

Publisher's full textScopus

Search in DiVA

By author/editor
Guillemot, C.
In the same journal
IEEE Transactions on Computational Imaging

Search outside of DiVA

GoogleGoogle Scholar

doi
urn-nbn

Altmetric score

doi
urn-nbn
Total: 3 hits
CiteExportLink to record
Permanent link

Direct link
Cite
Citation style
  • apa
  • ieee
  • modern-language-association-8th-edition
  • vancouver
  • Other style
More styles
Language
  • de-DE
  • en-GB
  • en-US
  • fi-FI
  • nn-NO
  • nn-NB
  • sv-SE
  • Other locale
More languages
Output format
  • html
  • text
  • asciidoc
  • rtf