Image super-resolution using deep feedforward neural networks in spectral domain
Author
Aydın, Onur
Advisor
Aksoy, Selim
Date
2018-03Publisher
Bilkent University
Language
English
Type
ThesisItem Usage Stats
243
views
views
145
downloads
downloads
Abstract
With recent advances in deep learning area, learning machinery and mainstream
approaches in computer vision research have changed dramatically from hardcoded
features combined with classi ers to end-to-end trained deep convolutional
neural networks (CNN) which give the state-of-the-art results in most of the
computer vision research areas. Single-image super-resolution is one of these areas
which are considerably in
uenced by deep learning advancements. Most of
the current state-of-the-art methods on super-resolution problem learn a nonlinear
mapping from low-resolution images to high-resolution images in the spatial
domain using consecutive convolutional layers in their network architectures.
However, these state-of-the-art results are obtained by training a separate neural
network architecture for each di erent scale factor. We propose a novel singleimage
super-resolution system with the limited number of learning parameters
in spectral domain in order to eliminate the necessity to train a separate neural
network for each scale factor. As a spectral transform function which converts
images from the spatial domain to the frequency domain, discrete cosine transform
(DCT) which is a variant of discrete Fourier transform (DFT) is used. In
addition, in the post-processing step, an artifact reduction module is added for
removing ringing artifacts occurred due to spectral transformations. Even if the
peak signal-to-noise ratio (PSNR) measurement of our super-resolution system
is lower than current state-of-the-art methods, the spectral domain allows us to
develop a single model with a single dataset for any scale factor and relatively
obtain better structural similarity index (SSIM) results.