634 research outputs found
Statistical performance analysis of a fast super-resolution technique using noisy translations
It is well known that the registration process is a key step for
super-resolution reconstruction. In this work, we propose to use a
piezoelectric system that is easily adaptable on all microscopes and telescopes
for controlling accurately their motion (down to nanometers) and therefore
acquiring multiple images of the same scene at different controlled positions.
Then a fast super-resolution algorithm \cite{eh01} can be used for efficient
super-resolution reconstruction. In this case, the optimal use of images
for a resolution enhancement factor is generally not enough to obtain
satisfying results due to the random inaccuracy of the positioning system. Thus
we propose to take several images around each reference position. We study the
error produced by the super-resolution algorithm due to spatial uncertainty as
a function of the number of images per position. We obtain a lower bound on the
number of images that is necessary to ensure a given error upper bound with
probability higher than some desired confidence level.Comment: 15 pages, submitte
Real Time Turbulent Video Perfecting by Image Stabilization and Super-Resolution
Image and video quality in Long Range Observation Systems (LOROS) suffer from
atmospheric turbulence that causes small neighbourhoods in image frames to
chaotically move in different directions and substantially hampers visual
analysis of such image and video sequences. The paper presents a real-time
algorithm for perfecting turbulence degraded videos by means of stabilization
and resolution enhancement. The latter is achieved by exploiting the turbulent
motion. The algorithm involves generation of a reference frame and estimation,
for each incoming video frame, of a local image displacement map with respect
to the reference frame; segmentation of the displacement map into two classes:
stationary and moving objects and resolution enhancement of stationary objects,
while preserving real motion. Experiments with synthetic and real-life
sequences have shown that the enhanced videos, generated in real time, exhibit
substantially better resolution and complete stabilization for stationary
objects while retaining real motion.Comment: Submitted to The Seventh IASTED International Conference on
Visualization, Imaging, and Image Processing (VIIP 2007) August, 2007 Palma
de Mallorca, Spai
Superresolution imaging: A survey of current techniques
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
Superresolution Enhancement of Hyperspectral CHRIS/Proba Images With a Thin-Plate Spline Nonrigid Transform Model
Given the hyperspectral-oriented waveband configuration of multiangular CHRIS/Proba imagery, the scope of its application could widen if the present 18-m resolution would be improved. The multiangular images of CHRIS could be used as input for superresolution (SR) image reconstruction. A critical procedure in SR is an accurate registration of the low-resolution images. Conventional methods based on affine transformation may not be effective given the local geometric distortion in high off-nadir angular images. This paper examines the use of a non-rigid transform to improve the result of a nonuniform interpolation and deconvolution SR method. A scale-invariant feature transform is used to collect control points (CPs). To ensure the quality of CPs, a rigorous screening procedure is designed: 1) an ambiguity test; 2) the m-estimator sample consensus method; and 3) an iterative method using statistical characteristics of the distribution of random errors. A thin-plate spline (TPS) nonrigid transform is then used for the registration. The proposed registration method is examined with a Delaunay triangulation-based nonuniform interpolation and reconstruction SR method. Our results show that the TPS nonrigid transform allows accurate registration of angular images. SR results obtained from simulated LR images are evaluated using three quantitative measures, namely, relative mean-square error, structural similarity, and edge stability. Compared to the SR methods that use an affine transform, our proposed method performs better with all three evaluation measures. With a higher level of spatial detail, SR-enhanced CHRIS images might be more effective than the original data in various applications.JRC.H.7-Climate Risk Managemen
Advances in 3D single particle localization microscopy
The spatial resolution of conventional optical microscopy is limited by diffraction to transverse and axial resolutions of about 250 nm, but localization of point sources, such as single molecules or fluorescent beads, can be achieved with a precision of 10 nm or better in each direction. Traditional approaches to localization microscopy in two dimensions enable high precision only for a thin in-focus layer that is typically much less than the depth of a cell. This precludes, for example, super-resolution microscopy of extended three-dimensional biological structures or mapping of blood velocity throughout a useful depth of vasculature. Several techniques have been reported recently for localization microscopy in three dimensions over an extended depth range. We describe the principles of operation and typical applications of the most promising 3D localization microscopy techniques and provide a comparison of the attainable precision for each technique in terms of the Cramér-Rao lower bound for high-resolution imaging
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