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== Reference ==
Capel, David (2001). Image Mosaicing and Super-resolution. Ph. D. thesis, Oxford University, Dept. of Engineering Science. ISBN: 978-1-4471-1049-1 (Print) 978-0-85729-384-8 (Online)

== DOI ==
http://link.springer.com/book/10.1007%2F978-0-85729-384-8

== Abstract ==
The thesis investigates the problem of how information contained in multiple, overlap-
ping images of the same scene may be combined to produce images of superior quality.
This area, generically titled frame fusion, offers the possibility of reducing noise, extend-
ing the field of view, removal of moving objects, removing blur, increasing spatial resolu-
tion and improving dynamic range. As such, this research has many applications in fields
as diverse as forensic image restoration, computer generated special effects, video image
compression, and digital video editing.
An essential enabling step prior to performing frame fusion is image registration, by
which an accurate estimate of the point-to-point mapping between views is computed. A
robust and efficient algorithm is described to automatically register multiple images using
only information contained within the images themselves. The accuracy of this method,
and the statistical assumptions upon which it relies, are investigated empirically.
Two forms of frame-fusion are investigated. The first is image mosaicing, which is the
alignment of multiple images into a single composition representing part of a 3D scene.
Various methods of presenting the composite image are demonstrated, and in particular,
a novel algorithm is developed for automatically choosing an optimal viewing transfor-
mation for certain cases. In addition, a new and efficient method is demonstrated for the
matching of point features across multiple views.
The second frame-fusion method is super-resolution, which aims to restore poor qual-
ity video sequences by removing the degradations inherent in the imaging process. The
framework presented here uses a generative model of the imaging process, which is dis-
cussed in detail and an efficient implementation presented. An algorithm is developed
which seeks a maximum likelihood estimate under this model, and the factors affecting its
performance are investigated analytically and empirically.
The use of “generic” prior image models in a Bayesian framework is described and
shown to produce dramatically improved super-resolution results. Finally, super-resolution
algorithms are developed which make use of image models which are tuned to specific
classes of image. These algorithms are shown to produce results of comparable or better
quality than those using generic priors, while having a lower computational complexity.
The technique is applied to images of text and faces.
Throughout this work, the performance of the algorithms is evaluated using real image
sequences. The applications demonstrated include the separation of latent marks from
cluttered, non-periodic backgrounds in forensic images; the automatic creation of full 360 ◦
panoramic mosaics; and the super-resolution restoration of various scenes, including text
and faces in low-quality video.

== Extended Abstract ==

== Bibtex ==

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== Links ==
=== Full Text ===
http://www.robots.ox.ac.uk/~vgg/publications/papers/capel01a.pdf

[[intern file]]

=== Sonstige Links ===

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