Learning Light Field Angular Super-Resolution via a Geometry-Aware Network

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

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Detail(s)

Original languageEnglish
Title of host publicationThe Thirty-Fourth AAAI Conference on Artificial Intelligence (AAAI-20)
Place of PublicationCalifornia
PublisherAAAI Press
Pages11141-11148
ISBN (Print)9781577358350 (set)
Publication statusPublished - Feb 2020

Publication series

NameProceedings of the AAAI Conference on Artificial Intelligence
PublisherAAAI Press
Number7
Volume34
ISSN (Print)2159-5399
ISSN (Electronic)2374-3468

Conference

Title34th AAAI Conference on Artificial Intelligence (AAAI-20)
PlaceUnited States
CityNew York
Period7 - 12 February 2020

Abstract

The acquisition of light field images with high angular resolution is costly. Although many methods have been proposed to improve the angular resolution of a sparsely-sampled light field, they always focus on the light field with a small baseline, which is captured by a consumer light field camera. By making full use of the intrinsic geometry information of light fields, in this paper we propose an end-to-end learning-based approach aiming at angularly super-resolving a sparsely-sampled light field with a large baseline. Our model consists of two learnable modules and a physically-based module. Specifically, it includes a depth estimation module for explicitly modeling the scene geometry, a physically-based warping for novel views synthesis, and a light field blending module specifically designed for light field reconstruction. Moreover, we introduce a novel loss function to promote the preservation of the light field parallax structure. Experimental results over various light field datasets including large baseline light field images demonstrate the significant superiority of our method when compared with state-of-the-art ones, i.e., our method improves the PSNR of the second best method up to 2 dB in average, while saves the execution time 48×. In addition, our method preserves the light field parallax structure better.

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Citation Format(s)

Learning Light Field Angular Super-Resolution via a Geometry-Aware Network. / Jin, Jing; Hou, Junhui; Yuan, Hui; Kwong, Sam.

The Thirty-Fourth AAAI Conference on Artificial Intelligence (AAAI-20). California : AAAI Press, 2020. p. 11141-11148 (Proceedings of the AAAI Conference on Artificial Intelligence; Vol. 34, No. 7).

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