Virtual focus and depth estimation from defocused video sequences

In this paper, we present a novel method for virtual focus and object depth estimation from defocused video captured by a moving camera. We use the term virtual focus to refer to a new approach for producing in-focus image sequences by processing blurred videos captured by out-of-focus cameras. Our...

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Veröffentlicht in:IEEE transactions on image processing : a publication of the IEEE Signal Processing Society. - 1992. - 19(2010), 3 vom: 01. März, Seite 668-79
1. Verfasser: Yang, Junlan (VerfasserIn)
Weitere Verfasser: Schonfeld, Dan
Format: Online-Aufsatz
Sprache:English
Veröffentlicht: 2010
Zugriff auf das übergeordnete Werk:IEEE transactions on image processing : a publication of the IEEE Signal Processing Society
Schlagworte:Journal Article Research Support, Non-U.S. Gov't
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520 |a In this paper, we present a novel method for virtual focus and object depth estimation from defocused video captured by a moving camera. We use the term virtual focus to refer to a new approach for producing in-focus image sequences by processing blurred videos captured by out-of-focus cameras. Our method relies on the concept of Depth-from-Defocus (DFD) for virtual focus estimation. However, the proposed approach overcomes limitations of DFD by reformulating the problem in a moving-camera scenario. We introduce the interframe image motion model, from which the relationship between the camera motion and blur characteristics can be formed. This relationship subsequently leads to a new method for blur estimation. We finally rely on the blur estimation to develop the proposed technique for object depth estimation and focused video reconstruction. The proposed approach can be utilized to correct out-of-focus video sequences and can potentially replace the expensive apparatus required for auto-focus adjustments currently employed in many camera devices. The performance of the proposed algorithm is demonstrated through error analysis and computer simulated experiments 
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