Authors:
Kaoning Hu
1
;
Dongeun Lee
1
and
Tianyang Wang
2
Affiliations:
1
Department of Computer Science and Information Systems, Texas A&M University - Commerce, Commerce, Texas, U.S.A.
;
2
Department of Computer Science and Information Technology, Austin Peay State University, Clarksville, Tennessee, U.S.A.
Keyword(s):
SISR, Image Vectorization, Texture Synthesis, KS Test.
Abstract:
Image super-resolution is a very useful tool in science and art. In this paper, we propose a novel method for single image super-resolution that combines image vectorization and texture synthesis. Image vectorization is the conversion from a raster image to a vector image. While image vectorization algorithms can trace the fine edges of images, they will sacrifice color and texture information. In contrast, texture synthesis techniques, which have been previously used in image super-resolution, can reasonably create high-resolution color and texture information, except that they sometimes fail to trace the edges of images correctly. In this work, we adopt the image vectorization to the edges of the original image, and the texture synthesis based on the Kolmogorov–Smirnov test (KS test) to the non-edge regions of the original image. The goal is to generate a plausible, visually pleasing detailed higher resolution version of the original image. In particular, our method works very well
on the images of natural animals.
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