Abstract
The problem of resolution enhancement in images from multiple low-resolution captures has garnered significant attention over the last decade. While initial algorithms estimated the unknown high-resolution (hi-res) image for a fixed set of imaging model parameters, significant recent advances have been in simultaneous maximum aposteriori (MAP) estimation of the hi-res image as well as the geometric registration parameters under a variety of noise and prior models. A key computational challenge however, lies in the algorithmic tractability of the resulting optimization problem. Independently, there has been a surge in approaches for enhancing amplitude (or dynamic range) resolution in images from multiple captures. We develop a novel constrained optimization framework to address the problem of joint estimation of imaging model parameters and the unknown hi-res, high dynamic range image. In this framework, we employ a transformation of variables to establish separable convexity of the cost function under any lp norm, p ≥ 1, in the individual variables of geometric and photometric registration parameters, optical blur and the unknown hi-res image. We formulate evolving convex constraints which ensure that the registration parameters as well as the reconstructed image remain physically meaningful. The convergence guarantee afforded by our algorithm alleviates unreasonable demands on initialization, and produces reconstructed image results approaching practical upper bounds. Several existing formulations reduce to special cases of our framework making the algorithm broadly applicable.
Original language | English (US) |
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Title of host publication | Conference Record of the 44th Asilomar Conference on Signals, Systems and Computers, Asilomar 2010 |
Pages | 870-874 |
Number of pages | 5 |
DOIs | |
State | Published - Dec 1 2010 |
Event | 44th Asilomar Conference on Signals, Systems and Computers, Asilomar 2010 - Pacific Grove, CA, United States Duration: Nov 7 2010 → Nov 10 2010 |
Other
Other | 44th Asilomar Conference on Signals, Systems and Computers, Asilomar 2010 |
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Country/Territory | United States |
City | Pacific Grove, CA |
Period | 11/7/10 → 11/10/10 |
All Science Journal Classification (ASJC) codes
- Signal Processing
- Computer Networks and Communications