International Journal of Computer Vision
An MRF Model-Based Approach to Simultaneous Recovery of Depth and Restoration from Defocused Images
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
A Geometric Approach to Shape from Defocus
IEEE Transactions on Pattern Analysis and Machine Intelligence
On defocus, diffusion and depth estimation
Pattern Recognition Letters
Virtual focus and depth estimation from defocused video sequences
IEEE Transactions on Image Processing
Coded Aperture Pairs for Depth from Defocus and Defocus Deblurring
International Journal of Computer Vision
Depth and deblurring from a spectrally-varying depth-of-field
ECCV'12 Proceedings of the 12th European conference on Computer Vision - Volume Part V
Optimized aperture shapes for depth estimation
Pattern Recognition Letters
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In the depth from defocus (DFD) method, two defocused images of a scene are obtained by capturing the scene with different sets of camera parameters. An arbitrary selection of the camera settings can result in observed images whose relative blurring is insufficient to yield a good estimate of the depth. In this paper, we study the effect of the degree of relative blurring on the accuracy of the estimate of the depth by addressing the DFD problem in a maximum likelihood-based framework. We propose a criterion for optimal selection of camera parameters to obtain an improved estimate of the depth. The optimality criterion is based on the Cramer-Rao bound of the variance of the error in the estimate of blur. Simulations as well as experimental results on real images are presented for validation.