Superresolution image reconstruction from blurred observations by multisensors

✍ Scribed by Wai-Ki Ching; Michael K. Ng; Kenton N. Sze; Andy C. Yau

• Publisher: John Wiley and Sons
• Year: 2003
• Tongue: English
• Weight: 297 KB
• Volume: 13
• Category: Article
• ISSN: 0899-9457
• DOI: 10.1002/ima.10053

✦ Synopsis

Abstract

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 H^1^‐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