Regularized depth from defocus

Vinay P. Namboodiri, Subhasis Chaudhuri, Sunil Hadap

Research output: Chapter or section in a book/report/conference proceedingChapter in a published conference proceeding

23 Citations (SciVal)


In the area of depth estimation from images an interesting approach has been structure recovery from defocus cue. Towards this end, there have been a number of approaches [4, 6]. Here we propose a technique to estimate the regularized depth from defocus using diffusion. The coefficient of the diffusion equation is modeled using a pair-wise Markov random field (MRF) ensuring spatial regularization to enhance the robustness of the depth estimated. This framework is solved efficiently using a graph-cuts based techniques. The MRF representation is enhanced by incorporating a smoothness prior that is obtained from a graph based segmentation of the input images. The method is demonstrated on a number of data sets and its performance is compared with state of the art techniques.

Original languageEnglish
Title of host publication2008 IEEE International Conference on Image Processing, ICIP 2008 Proceedings
Number of pages4
ISBN (Print)1424417643, 9781424417643
Publication statusPublished - 12 Dec 2008
Event2008 IEEE International Conference on Image Processing, ICIP 2008 - San Diego, CA, USA United States
Duration: 12 Oct 200815 Oct 2008

Publication series

NameProceedings - International Conference on Image Processing, ICIP
ISSN (Print)1522-4880


Conference2008 IEEE International Conference on Image Processing, ICIP 2008
Country/TerritoryUSA United States
CitySan Diego, CA


  • Defocus
  • Depth from defocus
  • Focus
  • Graph-cuts

ASJC Scopus subject areas

  • Software
  • Computer Vision and Pattern Recognition
  • Signal Processing


Dive into the research topics of 'Regularized depth from defocus'. Together they form a unique fingerprint.

Cite this