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)

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Author(s)

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Detail(s)

Original languageEnglish
Title of host publicationIEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR 2020)
PublisherIEEE
Number of pages10
Publication statusPublished - Jun 2020

Conference

Title2020 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR 2020)
LocationVirtual
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

This paper was accepted by CVPR 2020. Since this conference is yet to commence, the information for this record is subject to revision.

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.

IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR 2020). IEEE, 2020.

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