Depth-Aware Motion Deblurring Using Loopy Belief Propagation

Bin Sheng, Ping Li, Xiaoxin Fang, Ping Tan, Enhua Wu

Research output: Journal article publicationJournal articleAcademic researchpeer-review

5 Citations (Scopus)

Abstract

Most motion-blurred images captured in the real world have spatially-varying point-spread functions, and some are caused by different positions and depth values, which cannot be handled by most state-of-the-art deblurring methods based on deconvolution. To overcome this problem, we propose a depth-aware motion blur model that treats a blurred image as an integration of a sequence of clear images. To restore the clear latent image, we extend the Richardson-Lucy method to incorporate our blur model with a given depth image. The empty holes in the depth image, caused by occlusion or device limitations, are fixed by PatchMatch-based depth filling. We regard the depth image as a Markov random field and select candidate labels by using belief propagation to set and smooth depth values for empty areas. Deblurring and depth filling are performed iteratively to refine the results. Our method can also be applied to real-world images with the assistance of motion estimation. The deblurring process is shown to be convergent; moreover, the number of iterations and the level of noise amplification are acceptable. The experimental results show that our method can not only handle depth-variant motion blur but also refine depth images.

Original languageEnglish
Article number8653352
Pages (from-to)955-969
Number of pages15
JournalIEEE Transactions on Circuits and Systems for Video Technology
Volume30
Issue number4
DOIs
Publication statusPublished - Apr 2020
Externally publishedYes

Keywords

  • Deblur
  • depth-variant
  • Richardson-Lucy

ASJC Scopus subject areas

  • Media Technology
  • Electrical and Electronic Engineering

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