A New Sense for Depth of Field
IEEE Transactions on Pattern Analysis and Machine Intelligence
Depth from defocus: a spatial domain approach
International Journal of Computer Vision
Space-variant approaches to recovery of depth from defocused images
Computer Vision and Image Understanding
Rational Filters for Passive Depth from Defocus
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
IEEE Transactions on Pattern Analysis and Machine Intelligence
Observing Shape from Defocused Images
International Journal of Computer Vision
ECCV '02 Proceedings of the 7th European Conference on Computer Vision-Part II
A block shift-variant blur model for recovering depth from defocused images
ICIP '95 Proceedings of the 1995 International Conference on Image Processing (Vol. 3)-Volume 3 - Volume 3
High-quality video view interpolation using a layered representation
ACM SIGGRAPH 2004 Papers
Two motion-blurred images are better than one
Pattern Recognition Letters - Special issue: In memoriam Azriel Rosenfeld
3-D Shape Estimation and Image Restoration: Exploiting Defocus and Motion-Blur
3-D Shape Estimation and Image Restoration: Exploiting Defocus and Motion-Blur
On defocus, diffusion and depth estimation
Pattern Recognition Letters
Image and depth from a conventional camera with a coded aperture
ACM SIGGRAPH 2007 papers
Shape from Defocus via Diffusion
IEEE Transactions on Pattern Analysis and Machine Intelligence
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This paper presents a novel iterative feedback framework for simultaneous estimation of depth map and All-In-Focus (AIF) image, which benefits each other in each stage to obtain final convergence: For the recovery of AIF image, sparse prior of natural image is incorporated to ensure high quality defocus removal even under inaccurate depth estimation. In depth estimation step, we feed back the constraints from the high quality AIF image and adopt a numerical solution which is robust to the inaccuracy of AIF recovery to further raise the performance of DFD algorithm. Compared with traditional DFD methods, another advantage offered by this iterative framework is that by introducing AIF, which follows the prior knowledge of natural images to regularize the depth map estimation, DFD is much more robust to camera parameter changes. In addition, the proposed approach is a general framework that can incorporate depth estimation and AIF image recovery algorithms. The experimental results on both synthetic and real images demonstrate the effectiveness of the proposed method, especially on the challenging data sets containing large textureless regions and within a large range of camera parameters.