TY - GEN
T1 - Image super-resolution using gradient profile prior
AU - Sun, Jian
AU - Sun, Jian
AU - Xu, Zongben
AU - Shum, Heung Yeung
PY - 2008
Y1 - 2008
N2 - In this paper, we propose an image super-resolution approach using a novel generic image prior - gradient profile prior, which is a parametric prior describing the shape and the sharpness of the image gradients. Using the gradient profile prior learned from a large number of natural images, we can provide a constraint on image gradients when we estimate a hi-resolution image from a low-resolution image. With this simple but very effective prior, we are able to produce state-of-the-art results. The reconstructed hi-resolution image is sharp while has rare ringing or jaggy artifacts.
AB - In this paper, we propose an image super-resolution approach using a novel generic image prior - gradient profile prior, which is a parametric prior describing the shape and the sharpness of the image gradients. Using the gradient profile prior learned from a large number of natural images, we can provide a constraint on image gradients when we estimate a hi-resolution image from a low-resolution image. With this simple but very effective prior, we are able to produce state-of-the-art results. The reconstructed hi-resolution image is sharp while has rare ringing or jaggy artifacts.
UR - https://www.scopus.com/pages/publications/51949110386
U2 - 10.1109/CVPR.2008.4587659
DO - 10.1109/CVPR.2008.4587659
M3 - 会议稿件
AN - SCOPUS:51949110386
SN - 9781424422432
T3 - 26th IEEE Conference on Computer Vision and Pattern Recognition, CVPR
BT - 26th IEEE Conference on Computer Vision and Pattern Recognition, CVPR
T2 - 26th IEEE Conference on Computer Vision and Pattern Recognition, CVPR
Y2 - 23 June 2008 through 28 June 2008
ER -