Abstract
We describe a new algorithm for combining multiple low-resolution images to obtain a high-resolution object estimate. Each camera is treated as a communication channel and we exploit sub-pixel shifts to achieve significant resolution enhancement. The 2D4 algorithm is an iterative likelihood-based method that is computationally less expensive than the two-dimensional Viterbi algorithm. In this paper, we modify the 2D4 algorithm and apply it to the multiframe image restoration problem. We demonstrate the reconstruction of a high-resolution scene from multiple blurred, noisy, and shifted low-resolution image measurements. We discuss the modifications and approximations to the 2D4 algorithm that are required to reduce its complexity for this application. We present the performance of this algorithm and compare it with the performance of Iterative Back Projection and optimal linear methods.
Original language | English (US) |
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Article number | 01 |
Pages (from-to) | 1-6 |
Number of pages | 6 |
Journal | Proceedings of SPIE - The International Society for Optical Engineering |
Volume | 5817 |
DOIs | |
State | Published - 2005 |
Externally published | Yes |
Event | Visual Information Processing XIV - Orlando, FL, United States Duration: Mar 29 2005 → Mar 30 2005 |
Keywords
- 2D4 algorithm
- Communication theoretic image restoration
- Multiframe image restoration
ASJC Scopus subject areas
- Electronic, Optical and Magnetic Materials
- Condensed Matter Physics
- Computer Science Applications
- Applied Mathematics
- Electrical and Electronic Engineering