TY - GEN
T1 - A constrained optimization perspective on joint spatial resolution and dynamic range enhancement
AU - Monga, Vishal
AU - Srinivas, Umamahesh
PY - 2010
Y1 - 2010
N2 - 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.
AB - 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.
UR - http://www.scopus.com/inward/record.url?scp=79958004287&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=79958004287&partnerID=8YFLogxK
U2 - 10.1109/ACSSC.2010.5757691
DO - 10.1109/ACSSC.2010.5757691
M3 - Conference contribution
AN - SCOPUS:79958004287
SN - 9781424497218
T3 - Conference Record - Asilomar Conference on Signals, Systems and Computers
SP - 870
EP - 874
BT - Conference Record of the 44th Asilomar Conference on Signals, Systems and Computers, Asilomar 2010
T2 - 44th Asilomar Conference on Signals, Systems and Computers, Asilomar 2010
Y2 - 7 November 2010 through 10 November 2010
ER -