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|Title:||On the recovery of depth from a single defocused image||Authors:||Zhuo, S.
Markov random field
|Issue Date:||2009||Citation:||Zhuo, S.,Sim, T. (2009). On the recovery of depth from a single defocused image. Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) 5702 LNCS : 889-897. ScholarBank@NUS Repository. https://doi.org/10.1007/978-3-642-03767-2_108||Abstract:||In this paper we address the challenging problem of recovering the depth of a scene from a single image using defocus cue. To achieve this, we first present a novel approach to estimate the amount of spatially varying defocus blur at edge locations. We re-blur the input image and show that the gradient magnitude ratio between the input and re-blurred images depends only on the amount of defocus blur. Thus, the blur amount can be obtained from the ratio. A layered depth map is then extracted by propagating the blur amount at edge locations to the entire image. Experimental results on synthetic and real images demonstrate the effectiveness of our method in providing a reliable estimate of the depth of a scene. © 2009 Springer Berlin Heidelberg.||Source Title:||Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)||URI:||http://scholarbank.nus.edu.sg/handle/10635/40895||ISBN:||3642037666||ISSN:||03029743||DOI:||10.1007/978-3-642-03767-2_108|
|Appears in Collections:||Staff Publications|
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