Learning-Based Parallax Transfer on Multispectral Light Field

Shengyu Nan, Jie Chen, Lap Pui Chau, Kemao Qian

Research output: Chapter in book / Conference proceedingConference article published in proceeding or bookAcademic researchpeer-review

Abstract

In light field imaging, one challenging task is to fuse the spatial and angular information with spectral information. Existing methods for multi-modal and multi-spectral correspondence problem are descriptors based algorithms which are time consuming and not able to handle the small base line issue in light field. In recent years, deep learning based methods demonstrate good performance in many challenging computer vision areas. Inspired by their powerful performance, we propose a learning based method to transfer the parallax information across channels in light field. We exploit spatial and angular information from two reference channels and the spatial information from the target channel to predict the different views and finally reconstruct the target channel. Experimental results demonstrate that compared with other descriptors based methods, our learning based method is much less time consuming and able to effectively transfer the parallax information even when the parallax shift is very small.

Original languageEnglish
Title of host publication2018 IEEE 23rd International Conference on Digital Signal Processing, DSP 2018
PublisherInstitute of Electrical and Electronics Engineers Inc.
ISBN (Electronic)9781538668115
DOIs
Publication statusPublished - 31 Jan 2019
Externally publishedYes
Event23rd IEEE International Conference on Digital Signal Processing, DSP 2018 - Shanghai, China
Duration: 19 Nov 201821 Nov 2018

Publication series

NameInternational Conference on Digital Signal Processing, DSP
Volume2018-November

Conference

Conference23rd IEEE International Conference on Digital Signal Processing, DSP 2018
Country/TerritoryChina
CityShanghai
Period19/11/1821/11/18

Keywords

  • deep learning
  • light field
  • multispectral
  • parallax transfer

ASJC Scopus subject areas

  • Signal Processing

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