TY - GEN
T1 - A super-resolution algorithm using linear regression based on image self-similarity
AU - Tai, Shen Chuan
AU - Huang, Jiun Jie
AU - Chen, Peng Yu
N1 - Publisher Copyright:
© 2016 IEEE.
PY - 2016/8/16
Y1 - 2016/8/16
N2 - The application of image super-resolution technologies in recent years has increased noticeably. The main purpose of image up-scaling is to obtain high-resolution images from low-resolution images, and these up-scaled images should keep satisfactory visual qualities and present natural textures. The most popular image up-scaling algorithms are based on interpolation methods in spatial domain. However, the up-scaled images may produce blurring artifacts. Therefore, using spatial sharpening filters is usually used to make blurred images sharp and clear. The quantity of image sharpening is the key to decide the visual qualities of up-scaled images. In this paper, a method based on self-similarity of images and using simple linear regression to build a reconstruction model for improving visual qualities of up-scaled images adaptively is proposed. The experimental results show that our algorithm provides better subjective visual qualities as well as the peak signal-to-noise ratio (PSNR).
AB - The application of image super-resolution technologies in recent years has increased noticeably. The main purpose of image up-scaling is to obtain high-resolution images from low-resolution images, and these up-scaled images should keep satisfactory visual qualities and present natural textures. The most popular image up-scaling algorithms are based on interpolation methods in spatial domain. However, the up-scaled images may produce blurring artifacts. Therefore, using spatial sharpening filters is usually used to make blurred images sharp and clear. The quantity of image sharpening is the key to decide the visual qualities of up-scaled images. In this paper, a method based on self-similarity of images and using simple linear regression to build a reconstruction model for improving visual qualities of up-scaled images adaptively is proposed. The experimental results show that our algorithm provides better subjective visual qualities as well as the peak signal-to-noise ratio (PSNR).
UR - https://www.scopus.com/pages/publications/84986224228
UR - https://www.scopus.com/pages/publications/84986224228#tab=citedBy
U2 - 10.1109/IS3C.2016.79
DO - 10.1109/IS3C.2016.79
M3 - Conference contribution
AN - SCOPUS:84986224228
T3 - Proceedings - 2016 IEEE International Symposium on Computer, Consumer and Control, IS3C 2016
SP - 275
EP - 278
BT - Proceedings - 2016 IEEE International Symposium on Computer, Consumer and Control, IS3C 2016
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 2016 IEEE International Symposium on Computer, Consumer and Control, IS3C 2016
Y2 - 4 July 2016 through 6 July 2016
ER -