Cristóbal, G., Gil, E., Šroubek, F., Flusser, J., Miravet, C., Rodríguez, F. B., “Superresolution imaging: A survey of current techniques”, Proceedings of SPIE - The International Society for Optical Engineering, 7074, 2008. Copyright 2008. Society of Photo Optical Instrumentation Engineers. One print or electronic copy may be made for personal use only. Systematic reproduction and distribution, duplication of any material in this paper for a fee or for commercial purposes, or modification of the content of the paper are prohibited.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 insufficient 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.This research has been partially supported by the following grants: TEC2007-67025/TCM, TEC2006-28009-E,
BFI-2003-07276, TIN-2004-04363-C03-03 by the Spanish Ministry of Science and Innovation, and by PROFIT
projects FIT-070000-2003-475 and FIT-330100-2004-91. Also, this work has been partially supported by the
Czech Ministry of Education under the project No. 1M0572 (Research Center DAR) and by the Czech Science
Foundation under the project No. GACR 102/08/1593 and the CSIC-CAS bilateral project 2006CZ002