Share Email Print

Proceedings Paper

Superresolution imaging: a survey of current techniques
Author(s): G. Cristóbal; E. Gil; F. Šroubek; J. Flusser; C. Miravet; F. B. Rodríguez
Format Member Price Non-Member Price
PDF $14.40 $18.00
cover GOOD NEWS! Your organization subscribes to the SPIE Digital Library. You may be able to download this paper for free. Check Access

Paper Abstract

Imaging plays a key role in many diverse areas of application, such as astronomy, remote sensing, microscopy, and tomography. Owing to imperfections of measuring devices (e.g., optical degradations, limited size of sensors) and instability of the observed scene (e.g., object motion, media turbulence), acquired images can be indistinct, noisy, and may exhibit insuffcient spatial and temporal resolution. In particular, several external effects blur images. Techniques for recovering the original image include blind deconvolution (to remove blur) and superresolution (SR). The stability of these methods depends on having more than one image of the same frame. Differences between images are necessary to provide new information, but they can be almost unperceivable. State-of-the-art SR techniques achieve remarkable results in resolution enhancement by estimating the subpixel shifts between images, but they lack any apparatus for calculating the blurs. In this paper, after introducing a review of current SR techniques we describe two recently developed SR methods by the authors. First, we introduce a variational method that minimizes a regularized energy function with respect to the high resolution image and blurs. In this way we establish a unifying way to simultaneously estimate the blurs and the high resolution image. By estimating blurs we automatically estimate shifts with subpixel accuracy, which is inherent for good SR performance. Second, an innovative learning-based algorithm using a neural architecture for SR is described. Comparative experiments on real data illustrate the robustness and utilization of both methods.

Paper Details

Date Published: 3 September 2008
PDF: 18 pages
Proc. SPIE 7074, Advanced Signal Processing Algorithms, Architectures, and Implementations XVIII, 70740C (3 September 2008); doi: 10.1117/12.797302
Show Author Affiliations
G. Cristóbal, Instituto de Optica (Spain)
E. Gil, Instituto de Optica (Spain)
F. Šroubek, Institute of Information Theory and Automation of the ASCR (Czech Republic)
J. Flusser, Institute of Information Theory and Automation of the ASCR (Czech Republic)
C. Miravet, Univ. Autónoma (Spain)
F. B. Rodríguez, Univ. Autónoma (Spain)

Published in SPIE Proceedings Vol. 7074:
Advanced Signal Processing Algorithms, Architectures, and Implementations XVIII
Franklin T. Luk, Editor(s)

© SPIE. Terms of Use
Back to Top