A New Sense for Depth of Field
IEEE Transactions on Pattern Analysis and Machine Intelligence
Space-variant approaches to recovery of depth from defocused images
Computer Vision and Image Understanding
Degraded Image Analysis: An Invariant Approach
IEEE Transactions on Pattern Analysis and Machine Intelligence
Rational Filters for Passive Depth from Defocus
International Journal of Computer Vision
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 Taxonomy and Evaluation of Dense Two-Frame Stereo Correspondence Algorithms
International Journal of Computer Vision
Homotopy-Based Estimation of Depth Cues in Spatial Domain
ICPR '02 Proceedings of the 16 th International Conference on Pattern Recognition (ICPR'02) Volume 3 - Volume 3
A Maximum-Flow Formulation of the N-Camera Stereo Correspondence Problem
ICCV '98 Proceedings of the Sixth International Conference on Computer Vision
Optimal Recovery of Depth from Defocused Images Using an MRF Model
ICCV '98 Proceedings of the Sixth International Conference on Computer Vision
IEEE Transactions on Pattern Analysis and Machine Intelligence
Active estimation of distance in a robotic system that replicates human eye movement
Robotics and Autonomous Systems
Blur and Contrast Invariant Fast Stereo Matching
ACIVS '08 Proceedings of the 10th International Conference on Advanced Concepts for Intelligent Vision Systems
Real Aperture Axial Stereo: Solving for Correspondences in Blur
Proceedings of the 31st DAGM Symposium on Pattern Recognition
Virtual focus and depth estimation from defocused video sequences
IEEE Transactions on Image Processing
View interpolation using defocused stereo images: a space-invariant filtering approach
ICIP'09 Proceedings of the 16th IEEE international conference on Image processing
Depth recovery from motion and defocus blur
ICIAR'06 Proceedings of the Third international conference on Image Analysis and Recognition - Volume Part II
Perceptual depth estimation from a single 2d image based on visual perception theory
PCM'06 Proceedings of the 7th Pacific Rim conference on Advances in Multimedia Information Processing
Towards Unrestrained Depth Inference with Coherent Occlusion Filling
International Journal of Computer Vision
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We propose a method for estimating depth from images captured with a real aperture camera by fusing defocus and stereo cues. The idea is to use stereo-based constraints in conjunction with defocusing to obtain improved estimates of depth over those of stereo or defocus alone. The depth map as well as the original image of the scene are modeled as Markov random fields with a smoothness prior, and their estimates are obtained by minimizing a suitable energy function using simulated annealing. The main advantage of the proposed method, despite being computationally less efficient than the standard stereo or DFD method, is simultaneous recovery of depth as well as space-variant restoration of the original focused image of the scene.