Superresolution image reconstruction from blurred observations by multisensors.

Superresolution image reconstruction refers to obtaining an image at a resolution higher than that of the camera (sensor) used in recording the image. In this article, we present a joint minimization model with an objective function setup that comprises three terms: the data-fitting term (DFT), the regularization term for the reconstructed image, and the observed low-resolution images. An alternating minimization iterative algorithm is presented to reconstruct the image. We also analyze the alternating minimization iterative algorithm and show that it converges globally for H1-norm or total-variation regularization that are functional for the reconstructed image. Numeric examples are given to illustrate the effectiveness of the joint minimization model and the efficiency of the algorithm. © 2003 Wiley Periodicals, Inc. Int J Imaging Syst Technol 13, 153–160, 2003; Published online in Wiley InterScience (www.interscience.wiley.com). DOI 10.1002/ima.10053 [ABSTRACT FROM AUTHOR]