Image Synthesis With Efficient Defocus Blur for Stereoscopic Displays

Yi-Chun Chen*, Tian-Sheuan Chang

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

Abstract

Image synthesis for stereoscopic displays shall carefully control its depth of field to enhance perception while reduce visual fatigue due to accommodation-vergence mismatch. Existing approaches suffer from either lower quality due to occluding contour error, or high computational complexity. To meet above demands, this paper proposes a perceptual oriented defocus blur that reduces complexity with the depth layer processing and improves quality with transparency degree oriented superposition and edge enhancement. The simulation results show that the proposed approach has better quality than the conventional defocus blur methods and achieves similar quality compared to the deep learning based methods but with much lower complexity.

Original languageEnglish
Pages (from-to)176304-176312
Number of pages9
JournalIEEE Access
Volume8
DOIs
StatePublished - Sep 2020

Keywords

  • Optical filters
  • Rendering (computer graphics)
  • Image reconstruction
  • Convolution
  • Stereo image processing
  • Machine learning
  • Focusing
  • image filtering
  • image reconstruction
  • image texture
  • reconstruction algorithms
  • visual effects
  • VISUAL DISCOMFORT
  • COMPLEMENTARY CUES
  • DEPTH
  • DISPARITY

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