REGULARIZED DEPTH FROM DEFOCUS

被引：18

作者：

Namboodiri, Vinay P. ^{[1
]}

Chaudhuri, Subhasis ^{[2
]}

Hadap, Sunil ^{[3
]}

机构：

[1] Katholieke Univ Leuven, ESAT PSI VISICS, Kasteelpk Arenberg 10, B-3001 Heverlee, Belgium

[2] Indian Inst Technol, Dept Elect Engn, Bombay, Maharashtra, India

[3] Adobe Syst Inc, Adv Technol Labs, San Jose, CA USA

来源：

2008 15TH IEEE INTERNATIONAL CONFERENCE ON IMAGE PROCESSING, VOLS 1-5 | 2008年

关键词：

Focus; Defocus; Depth from Defocus; MAP-MRF; Graph-Cuts;

D O I：

10.1109/ICIP.2008.4712056

中图分类号：

TP18 [人工智能理论];

学科分类号：

081104 ; 0812 ; 0835 ; 1405 ;

摘要：

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.

引用

页码：1520 / 1523

页数：4

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