hide
Free keywords:
-
Abstract:
Super resolution is the task of reconstructing one or several high resolution
images, from one or several low resolution images. A variety of super resolution
methods have been proposed over the past three decades, some following a
singleframe based methodology while the others utilizing a multiple-frame based
one. These methods are usually very sensitive to their underlying model of data
and noise, which limits their performance. In this thesis, we propose and
compare
two multiple-frame based approaches that address such shortcomings. In the rst
proposal we investigate a fast, local approach which combines the low resolution
frames via warping and then performs diusion-based inpainting. The second
proposal models the image formation process in a variational framework with
regularization that is robust to errors in motion and blur estimation. In
addition, we
introduce a brightness adaptation step which results in images with sharper
edges.
An accurate estimation of optical ow among the low resolution measurements
is a fundamental step towards high quality super resolution for both methods.
Experiments conrm the eectiveness of our method on a variety of super
resolution
benchmark sequences, as well as its superiority in performance to other
closely-related methods.
