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Updated: May 26, 2026

High-resolution, High-speed, Three-dimensional Video Imaging with Digital Fringe Projection Techniques
Published on: December 3, 2013
Depth from motion and optical blur with an unscented Kalman filter
C Paramanand1, A N Rajagopalan
1Department of Electrical Engineering, Indian Institute of Technology Madras, Chennai, India. paramanand@gmail.com
This study recovers 3D scene structure from motion blur and optical defocus. It uses differences in blur between images to estimate depth, employing an unscented Kalman filter for accurate results.
Area of Science:
- Computer Vision
- Image Processing
- 3D Reconstruction
Background:
- Space-variant blur in images encodes depth information.
- Recovering 3D scene structure from blur is a challenging problem.
Purpose of the Study:
- To develop a novel method for 3D scene structure recovery using motion blur and optical defocus.
- To leverage the difference in blur between image observations as a depth cue.
Main Methods:
- Utilizes an unblurred-image/blurred-image pair for motion blur analysis.
- Employs a recursive state estimation framework, specifically an unscented Kalman filter, for depth estimation.
- Handles arbitrary blur kernel shapes and addresses depth from defocus with translational jitter.
Main Results:
- Successfully recovers depth information from space-variant blur.
- Demonstrates effectiveness on both synthetic and real-world data.
- Provides performance comparisons against existing techniques.
Conclusions:
- The proposed unscented Kalman filter approach effectively estimates scene depth from blur.
- The method is robust to different blur types and camera motion.
- Offers a viable solution for 3D reconstruction from blurred imagery.
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