DRLFNet : A Dense-Connection Residual Learning Neural Network for Light Field Super Resolution

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

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Author(s)

Related Research Unit(s)

Detail(s)

Original languageEnglish
Title of host publicationImage and Graphics
Subtitle of host publication11th International Conference, ICIG 2021, Haikou, China, August 6–8, 2021, Proceedings, Part III
EditorsYuxin Peng, Shi-Min Hu, Moncef Gabbouj, Kun Zhou, Michael Elad, Kun Xu
Place of PublicationCham
PublisherSpringer
Pages501-510
VolumePart III
ISBN (Electronic)9783030873615
ISBN (Print)9783030873608
Publication statusPublished - 2021

Publication series

NameLecture Notes in Computer Science
Volume12890
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349

Conference

Title11th International Conference on Image and Graphics (ICIG 2021)
LocationHaikou Eadry Royal Garden Hotel
PlaceChina
CityHaikou
Period6 - 8 August 2021

Abstract

Light field records both spatial and angular information of light rays. By using light field cameras, 3D scenes can be reconstructed easily for further virtual reality applications. Limited by the sensor size, there is a trade-off between the spatial and angular resolution. To address this problem, we propose a dense-connection residual learning neural network, namely DRLFNet, to super resolve light field images in spatial domain. The dense-connection residual learning is implemented based on the proposed dense-connection residual block (DResBlock) that is used to efficiently exploit the joint spatial and angular features and the hierarchical features in different layers. Experimental results demonstrate that the proposed method out-performs other state-of-the-art methods by a large margin in both visual and numerical evaluations.

Research Area(s)

  • Dense-connection, Light field images, Residual learning, Super-resolution

Bibliographic Note

Full text of this publication does not contain sufficient affiliation information. With consent from the author(s) concerned, the Research Unit(s) information for this record is based on the existing academic department affiliation of the author(s).

Citation Format(s)

DRLFNet: A Dense-Connection Residual Learning Neural Network for Light Field Super Resolution. / Fu, Congrui; Ma, Xin; Liu, Yao et al.
Image and Graphics: 11th International Conference, ICIG 2021, Haikou, China, August 6–8, 2021, Proceedings, Part III. ed. / Yuxin Peng; Shi-Min Hu; Moncef Gabbouj; Kun Zhou; Michael Elad; Kun Xu. Vol. Part III Cham: Springer, 2021. p. 501-510 (Lecture Notes in Computer Science; Vol. 12890).

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