TY - GEN
T1 - Centralized sparse representation for image restoration
AU - Dong, Weisheng
AU - Zhang, Lei
AU - Shi, Guangming
PY - 2011/12/1
Y1 - 2011/12/1
N2 - This paper proposes a novel sparse representation model called centralized sparse representation (CSR) for image restoration tasks. In order for faithful image reconstruction, it is expected that the sparse coding coefficients of the degraded image should be as close as possible to those of the unknown original image with the given dictionary. However, since the available data are the degraded (noisy, blurred and/or down-sampled) versions of the original image, the sparse coding coefficients are often not accurate enough if only the local sparsity of the image is considered, as in many existing sparse representation models. To make the sparse coding more accurate, a centralized sparsity constraint is introduced by exploiting the nonlocal image statistics. The local sparsity and the nonlocal sparsity constraints are unified into a variational framework for optimization. Extensive experiments on image restoration validated that our CSR model achieves convincing improvement over previous state-of-the-art methods.
AB - This paper proposes a novel sparse representation model called centralized sparse representation (CSR) for image restoration tasks. In order for faithful image reconstruction, it is expected that the sparse coding coefficients of the degraded image should be as close as possible to those of the unknown original image with the given dictionary. However, since the available data are the degraded (noisy, blurred and/or down-sampled) versions of the original image, the sparse coding coefficients are often not accurate enough if only the local sparsity of the image is considered, as in many existing sparse representation models. To make the sparse coding more accurate, a centralized sparsity constraint is introduced by exploiting the nonlocal image statistics. The local sparsity and the nonlocal sparsity constraints are unified into a variational framework for optimization. Extensive experiments on image restoration validated that our CSR model achieves convincing improvement over previous state-of-the-art methods.
UR - http://www.scopus.com/inward/record.url?scp=84863012902&partnerID=8YFLogxK
U2 - 10.1109/ICCV.2011.6126377
DO - 10.1109/ICCV.2011.6126377
M3 - Conference article published in proceeding or book
AN - SCOPUS:84863012902
SN - 9781457711015
T3 - Proceedings of the IEEE International Conference on Computer Vision
SP - 1259
EP - 1266
BT - 2011 International Conference on Computer Vision, ICCV 2011
T2 - 2011 IEEE International Conference on Computer Vision, ICCV 2011
Y2 - 6 November 2011 through 13 November 2011
ER -