Light Field Spatial Super-resolution via CNN Guided by A Single High-resolution RGB Image

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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Detail(s)

Original languageEnglish
Title of host publication2018 IEEE 23rd International Conference on Digital Signal Processing (DSP)
PublisherIEEE
ISBN (Electronic)9781538668115
ISBN (Print)9781538668122
Publication statusPublished - Nov 2018

Publication series

NameInternational Conference on Digital Signal Processing, DSP
Volume2018-November
ISSN (Print)1546-1874
ISSN (Electronic)2165-3577

Conference

Title23rd IEEE International Conference on Digital Signal Processing (DSP 2018)
LocationShanghaiTech University
PlaceChina
CityShanghai
Period19 - 21 November 2018

Abstract

Light field (LF) has proven to be promising in immersive representation of the real world. However, a major limitation of micro-lens array based LF camera is the low spatial resolution, due to the inherent trade-off between angular and spatial dimensions. In this paper, we propose a framework to show that a single high-resolution (HR) RGB image effectively improves the performance of LF spatial super-resolution. We adopt an end-to-end convolutional neural network, which takes a low-resolution (LR) light field image (LFI) and a single HR center view as inputs. The LFI provides the information about LF structure in angular domain, while the HR center view provides more details in spatial domain. Experimental results on 57 test LFIs with various challenging natural scenes demonstrate that our algorithm outperforms current state-of-the-art methods.

Research Area(s)

  • deep learning, Light field, super-resolution

Citation Format(s)

Light Field Spatial Super-resolution via CNN Guided by A Single High-resolution RGB Image. / Jin, Jing; Hou, Junhui; Chen, Jie; Yeung, Henry; Kwong, Sam.

2018 IEEE 23rd International Conference on Digital Signal Processing (DSP). IEEE, 2018. 8631859 (International Conference on Digital Signal Processing, DSP; Vol. 2018-November).

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