A New Sense for Depth of Field
IEEE Transactions on Pattern Analysis and Machine Intelligence
An Investigation of Methods for Determining Depth from Focus
IEEE Transactions on Pattern Analysis and Machine Intelligence
Two-dimensional imaging
Selecting the Optimal Focus Measure for Autofocusing and Depth-From-Focus
IEEE Transactions on Pattern Analysis and Machine Intelligence
Depth from Defocus vs. Stereo: How Different Really Are They?
International Journal of Computer Vision - Special issue on computer vision research at the Technion
Machine Vision for Inspection and Measurement
Machine Vision for Inspection and Measurement
Computer Vision: A Modern Approach
Computer Vision: A Modern Approach
Optimal Selection of Camera Parameters for Recovery of Depth from Defocused Images
CVPR '97 Proceedings of the 1997 Conference on Computer Vision and Pattern Recognition (CVPR '97)
The Optimal Axial Interval in Estimating Depth from Defocus
ICCV '99 Proceedings of the International Conference on Computer Vision-Volume 2 - Volume 2
Depth Estimation and Image Restoration Using Defocused Stereo Pairs
IEEE Transactions on Pattern Analysis and Machine Intelligence
Focused image recovery from two defocused images recorded with different camera settings
IEEE Transactions on Image Processing
Fast and robust multiframe super resolution
IEEE Transactions on Image Processing
Reconstructing arbitrarily focused images from two differently focused images using linear filters
IEEE Transactions on Image Processing
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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.