Back
Example-Based Learning for Single-Image Super-Resolution
This paper proposes a regression-based method for single-image super-resolution. Kernel ridge regression (KRR) is used to estimate the high-frequency details of the underlying high-resolution image. A sparse solution of KRR is found by combining the ideas of kernel matching pursuit and gradient descent, which allows time-complexity to be kept to a moderate level. To resolve the problem of ringing artifacts occurring due to the regularization effect, the regression results are post-processed using a prior model of a generic image class. Experimental results demonstrate the effectiveness of the proposed method.
@inproceedings{5091, title = {Example-Based Learning for Single-Image Super-Resolution}, journal = {Pattern Recognition: Proceedings of the 30th DAGM Symposium}, booktitle = {DAGM 2008}, abstract = {This paper proposes a regression-based method for single-image super-resolution. Kernel ridge regression (KRR) is used to estimate the high-frequency details of the underlying high-resolution image. A sparse solution of KRR is found by combining the ideas of kernel matching pursuit and gradient descent, which allows time-complexity to be kept to a moderate level. To resolve the problem of ringing artifacts occurring due to the regularization effect, the regression results are post-processed using a prior model of a generic image class. Experimental results demonstrate the effectiveness of the proposed method.}, pages = {456-463}, editors = {Rigoll, G. }, publisher = {Springer}, organization = {Max-Planck-Gesellschaft}, school = {Biologische Kybernetik}, address = {Berlin, Germany}, month = jun, year = {2008}, slug = {5091}, author = {Kim, KI. and Kwon, Y.}, month_numeric = {6} }