12,507 research outputs found
An Improved Observation Model for Super-Resolution under Affine Motion
Super-resolution (SR) techniques make use of subpixel shifts between frames
in an image sequence to yield higher-resolution images. We propose an original
observation model devoted to the case of non isometric inter-frame motion as
required, for instance, in the context of airborne imaging sensors. First, we
describe how the main observation models used in the SR literature deal with
motion, and we explain why they are not suited for non isometric motion. Then,
we propose an extension of the observation model by Elad and Feuer adapted to
affine motion. This model is based on a decomposition of affine transforms into
successive shear transforms, each one efficiently implemented by row-by-row or
column-by-column 1-D affine transforms.
We demonstrate on synthetic and real sequences that our observation model
incorporated in a SR reconstruction technique leads to better results in the
case of variable scale motions and it provides equivalent results in the case
of isometric motions
Hypernetwork functional image representation
Motivated by the human way of memorizing images we introduce their functional
representation, where an image is represented by a neural network. For this
purpose, we construct a hypernetwork which takes an image and returns weights
to the target network, which maps point from the plane (representing positions
of the pixel) into its corresponding color in the image. Since the obtained
representation is continuous, one can easily inspect the image at various
resolutions and perform on it arbitrary continuous operations. Moreover, by
inspecting interpolations we show that such representation has some properties
characteristic to generative models. To evaluate the proposed mechanism
experimentally, we apply it to image super-resolution problem. Despite using a
single model for various scaling factors, we obtained results comparable to
existing super-resolution methods
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