Abstract—In recent years, a number of super-resolution
techniques have been proposed. Most of these techniques
construct a high resolution image by either combining several
low resolution images at sub-pixel misalignments or by learning
correspondences between low and high resolution image pairs.
In this paper we present a stochastic super-resolution method
for color textures from a single image. The proposed algorithm
takes advantage of the repetitive nature of textures and the
existence of several similar patches within the texture, as well as
the color-intensity correlation that often exist in natural images.
In the first step of the algorithm the intensity component is
interpolated. For each pixel, the missing value is chosen
according to a probability distribution constructed from a
measure of similarity to other patches in the texture as well as
from local features and patch color similarity. In the second
stage, the color components are interpolated in a similar
manner, using patches of the color channels as well as the
already interpolated intensity values. Our conclusion is that the
proposed approach outperforms presently available methods.
Index Terms—Image processing, super resolution, texture
interpolation, color zooming.
This research was supported in part by Technion's fund #7110134 and by
the Ollendorff Minerva Center. Minerva is funded through the BMBF.
The authors are with the Department of Electrical Engineering, Technion,
Haifa 32000, Israel (e-mail: ykalit@tx.technion.ac.il,
mp@ee.technion.ac.il).
[PDF]
Cite: Yaron Kalit and Moshe Porat, "A Single-Image Super-Resolution Method for Texture Interpolation,"
International Journal of Future Computer and Communication vol. 2, no. 1 pp. 54-58, 2013.
