TY - GEN
T1 - Adaptive regularization for multiple image restoration using an extended total variations approach
AU - Kitchener, Matthew Andrew
AU - Bouzerdoum, Abdesselam
AU - Phung, Son Lam
PY - 2011
Y1 - 2011
N2 - In this paper a Variational Inequality method for multiple input, multiple output image restoration is presented using an extended Total Variations (TV) regularizer. This approach calculates an adaptive regularization parameter for each image based on their respective degradations. The proposed extended Total Variations regularizer combines both intra-image and inter-image pixel information for improved restoration performance. Hyperparameters for controlling this new TV measure are calculated using a Bayesian joint maximum a posteriori approach.
AB - In this paper a Variational Inequality method for multiple input, multiple output image restoration is presented using an extended Total Variations (TV) regularizer. This approach calculates an adaptive regularization parameter for each image based on their respective degradations. The proposed extended Total Variations regularizer combines both intra-image and inter-image pixel information for improved restoration performance. Hyperparameters for controlling this new TV measure are calculated using a Bayesian joint maximum a posteriori approach.
KW - Bayesian
KW - Image Restoration
KW - Multiple Image
KW - Total Variations
UR - https://www.scopus.com/pages/publications/84856282541
U2 - 10.1109/ICIP.2011.6116648
DO - 10.1109/ICIP.2011.6116648
M3 - Conference contribution
AN - SCOPUS:84856282541
SN - 9781457713033
T3 - Proceedings - International Conference on Image Processing, ICIP
SP - 697
EP - 700
BT - ICIP 2011
T2 - 2011 18th IEEE International Conference on Image Processing, ICIP 2011
Y2 - 11 September 2011 through 14 September 2011
ER -