Light Field Spatial Super-resolution via Deep Combinatorial Geometry Embedding and Structural Consistency Regularization

Research output: Chapters, Conference Papers, Creative and Literary Works (RGC: 12, 32, 41, 45)32_Refereed conference paper (with ISBN/ISSN)peer-review

2 Scopus Citations
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Detail(s)

Original languageEnglish
Title of host publication2020 IEEE/CVF Conference on Computer Vision and Pattern Recognition
Subtitle of host publicationProceedings
PublisherInstitute of Electrical and Electronics Engineers
Pages2257-2266
Number of pages10
ISBN (Electronic)978-1-7281-7168-5
ISBN (Print)978-1-7281-7169-2
Publication statusPublished - Jun 2020

Publication series

NameIEEE/CVF Conference on Computer Vision and Pattern Recognition, CVPR
PublisherInstitute of Electrical and Electronics Engineers
ISSN (Print)1063-6919
ISSN (Electronic)2575-7075

Conference

Title2020 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR 2020)
LocationVirtual
PlaceUnited States
CitySeattle
Period13 - 19 June 2020

Abstract

Light field (LF) images acquired by hand-held devices usually suffer from low spatial resolution as the limited sampling resources have to be shared with the angular dimension. LF spatial super-resolution (SR) thus becomes an indispensable part of the LF camera processing pipeline. The high-dimensionality characteristic and complex geometrical structure of LF images make the problem more challenging than traditional single-image SR. The performance of existing methods is still limited as they fail to thoroughly explore the coherence among LF views and are insufficient in accurately preserving the parallax structure of the scene. In this paper, we propose a novel learning-based LF spatial SR framework, in which each view of an LF image is first individually super-resolved by exploring the complementary information among views with combinatorial geometry embedding. For accurate preservation of the parallax structure among the reconstructed views, a regularization network trained over a structure-aware loss function is subsequently appended to enforce correct parallax relationships over the intermediate estimation. Our proposed approach is evaluated over datasets with a large number of testing images including both synthetic and real-world scenes. Experimental results demonstrate the advantage of our approach over state-of-the-art methods, i.e., our method not only improves the average PSNR by more than 1.0 dB but also preserves more accurate parallax details, at a lower computational cost.

Research Area(s)

  • cs.CV, eess.IV

Bibliographic Note

Information for this record is supplemented by the author(s) concerned.

Citation Format(s)

Light Field Spatial Super-resolution via Deep Combinatorial Geometry Embedding and Structural Consistency Regularization. / Jin, Jing; Hou, Junhui; Chen, Jie; Kwong, Sam.

2020 IEEE/CVF Conference on Computer Vision and Pattern Recognition: Proceedings. Institute of Electrical and Electronics Engineers, 2020. p. 2257-2266 (IEEE/CVF Conference on Computer Vision and Pattern Recognition, CVPR).

Research output: Chapters, Conference Papers, Creative and Literary Works (RGC: 12, 32, 41, 45)32_Refereed conference paper (with ISBN/ISSN)peer-review