A New Sense for Depth of Field
IEEE Transactions on Pattern Analysis and Machine Intelligence
Depth from focus using pyramid architecture
Pattern Recognition Letters
An Investigation of Methods for Determining Depth from Focus
IEEE Transactions on Pattern Analysis and Machine Intelligence
Depth from defocus: a spatial domain approach
International Journal of Computer Vision
International Journal of Computer Vision
Rational Filters for Passive Depth from Defocus
International Journal of Computer Vision
Optimization by Vector Space Methods
Optimization by Vector Space Methods
IEEE Transactions on Pattern Analysis and Machine Intelligence
On Observing Shape from Defocused Images
ICIAP '99 Proceedings of the 10th International Conference on Image Analysis and Processing
Total variation image restoration: numerical methods and extensions
ICIP '97 Proceedings of the 1997 International Conference on Image Processing (ICIP '97) 3-Volume Set-Volume 3 - Volume 3
The Optimal Axial Interval in Estimating Depth from Defocus
ICCV '99 Proceedings of the International Conference on Computer Vision-Volume 2 - Volume 2
Passive Depth From Defocus Using a Spatial Domain Approach
ICCV '98 Proceedings of the Sixth International Conference on Computer Vision
A Variational Approach to Shape from Defocus
ECCV '02 Proceedings of the 7th European Conference on Computer Vision-Part II
ECCV '02 Proceedings of the 7th European Conference on Computer Vision-Part II
3D shape from anisotropic diffusion
CVPR'03 Proceedings of the 2003 IEEE computer society conference on Computer vision and pattern recognition
Coded Aperture Pairs for Depth from Defocus and Defocus Deblurring
International Journal of Computer Vision
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We formulate the problem of reconstructing the shape and radiance of a scene as the minimization of the information divergence between blurred images, and propose an algorithm that is provably convergent and guarantees that the solution is admissible, in the sense of corresponding to a positive radiance and imaging kernel. The motivation for the use of information divergence comes from the work of Csiszár [5], while the fundamental elements of the proof of convergence come from work by Snyder et al. [14], extended to handle unknown imaging kernels (i.e. the shape of the scene).