Accurate light field depth estimation with superpixel regularization over partially occluded regions

Jie Chen, Junhui Hou, Yun Ni, Lap Pui Chau

Research output: Journal article publicationJournal articleAcademic researchpeer-review

67 Citations (Scopus)

Abstract

Depth estimation is a fundamental problem for light field photography applications. Numerous methods have been proposed in recent years, which either focus on crafting cost terms for more robust matching, or on analyzing the geometry of scene structures embedded in the epipolar-plane images. Significant improvements have been made in terms of overall depth estimation error; however, current state-of-the-art methods still show limitations in handling intricate occluding structures and complex scenes with multiple occlusions. To address these challenging issues, we propose a very effective depth estimation framework which focuses on regularizing the initial label confidence map and edge strength weights. Specifically, we first detect partially occluded boundary regions (POBR) via superpixel-based regularization. Series of shrinkage/reinforcement operations are then applied on the label confidence map and edge strength weights over the POBR. We show that after weight manipulations, even a low-complexity weighted least squares model can produce much better depth estimation than the state-of-the-art methods in terms of average disparity error rate, occlusion boundary precision-recall rate, and the preservation of intricate visual features.

Original languageEnglish
Pages (from-to)4889-4900
Number of pages12
JournalIEEE Transactions on Image Processing
Volume27
Issue number10
DOIs
Publication statusPublished - Oct 2018
Externally publishedYes

Keywords

  • Light field
  • partially occluded border region
  • superpixel
  • weight manipulation

ASJC Scopus subject areas

  • Software
  • Computer Graphics and Computer-Aided Design

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