@inproceedings{Hong1997,
abstract = {n this paper, we propose a spatially adaptive image restoration algorithm, using local statistics. The local variance, mean and maximum value are utilized to constraint the solution space. These parameters are computed at each iteration step using partially restored image. A parameter defined by the user determines the degree of local smoothness imposed on the solution. The resulting iterative algorithm exhibits increased convergence speed when compared with the nonadaptive algorithm.},
author = {Hong, Min-cheol and Stathaki, Tania and Katsaggelos, Aggelos K},
title = {{Iterative Regularized Image Restoration Using Local}},
url = {https://www.academia.edu/2704377/Iterative_regularized_image_restoration_using_local_constraints},
year = {1997}
}

{"_id":"nbdEh3mAJC8KjqAeP","bibbaseid":"hong-stathaki-katsaggelos-iterativeregularizedimagerestorationusinglocal-1997","author_short":["Hong, M.","Stathaki, T.","Katsaggelos, A. K"],"bibdata":{"bibtype":"inproceedings","type":"inproceedings","abstract":"n this paper, we propose a spatially adaptive image restoration algorithm, using local statistics. The local variance, mean and maximum value are utilized to constraint the solution space. These parameters are computed at each iteration step using partially restored image. A parameter defined by the user determines the degree of local smoothness imposed on the solution. The resulting iterative algorithm exhibits increased convergence speed when compared with the nonadaptive algorithm.","author":[{"propositions":[],"lastnames":["Hong"],"firstnames":["Min-cheol"],"suffixes":[]},{"propositions":[],"lastnames":["Stathaki"],"firstnames":["Tania"],"suffixes":[]},{"propositions":[],"lastnames":["Katsaggelos"],"firstnames":["Aggelos","K"],"suffixes":[]}],"title":"Iterative Regularized Image Restoration Using Local","url":"https://www.academia.edu/2704377/Iterative_regularized_image_restoration_using_local_constraints","year":"1997","bibtex":"@inproceedings{Hong1997,\nabstract = {n this paper, we propose a spatially adaptive image restoration algorithm, using local statistics. The local variance, mean and maximum value are utilized to constraint the solution space. These parameters are computed at each iteration step using partially restored image. A parameter defined by the user determines the degree of local smoothness imposed on the solution. The resulting iterative algorithm exhibits increased convergence speed when compared with the nonadaptive algorithm.},\nauthor = {Hong, Min-cheol and Stathaki, Tania and Katsaggelos, Aggelos K},\ntitle = {{Iterative Regularized Image Restoration Using Local}},\nurl = {https://www.academia.edu/2704377/Iterative_regularized_image_restoration_using_local_constraints},\nyear = {1997}\n}\n","author_short":["Hong, M.","Stathaki, T.","Katsaggelos, A. K"],"key":"Hong1997","id":"Hong1997","bibbaseid":"hong-stathaki-katsaggelos-iterativeregularizedimagerestorationusinglocal-1997","role":"author","urls":{"Paper":"https://www.academia.edu/2704377/Iterative_regularized_image_restoration_using_local_constraints"},"metadata":{"authorlinks":{}}},"bibtype":"inproceedings","biburl":"https://sites.northwestern.edu/ivpl/files/2023/06/IVPL_Updated_publications-1.bib","dataSources":["fvDEHD49E2ZRwE3fb","H7crv8NWhZup4d4by","DHqokWsryttGh7pJE","vRJd4wNg9HpoZSMHD","sYxQ6pxFgA59JRhxi","w2WahSbYrbcCKBDsC","XasdXLL99y5rygCmq","3gkSihZQRfAD2KBo3","t5XMbyZbtPBo4wBGS","bEpHM2CtrwW2qE8FP","teJzFLHexaz5AQW5z"],"keywords":[],"search_terms":["iterative","regularized","image","restoration","using","local","hong","stathaki","katsaggelos"],"title":"Iterative Regularized Image Restoration Using Local","year":1997}