Iterative Wiener Filtering for Deconvolution with Ringing Artifact Suppression. Šroubek, F., Kerepecký, T., & Kamenický, J. In 2019 27th European Signal Processing Conference (EUSIPCO), pages 1-5, Sep., 2019.
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Paper doi abstract bibtex
Paper doi abstract bibtex Sensor and lens blur degrade images acquired by digital cameras. Simple and fast removal of blur using linear filtering, such as Wiener filter, produces results that are not acceptable in most of the cases due to ringing artifacts close to image borders and around edges in the image. More elaborate deconvolution methods with non-smooth regularization, such as total variation, provide superior performance with less artifacts, however at a price of increased computational cost. We consider the alternating directions method of multipliers, which is a popular choice to solve such non-smooth convex problems, and show that individual steps of the method can be decomposed to simple filtering and element-wise operations. Filtering is performed with two sets of filters, called restoration and update filters, which are learned for the given type of blur and noise level with two different learning methods. The proposed deconvolution algorithm is implemented in the spatial domain and can be easily extended to include other restoration tasks such as demosaicing and super-resolution. Experiments demonstrate performance of the algorithm with respect to the size of learned filters, number of iterations, noise level and type of blur.
@InProceedings{8903114,
author = {F. {Šroubek} and T. Kerepecký and J. Kamenický},
booktitle = {2019 27th European Signal Processing Conference (EUSIPCO)},
title = {Iterative Wiener Filtering for Deconvolution with Ringing Artifact Suppression},
year = {2019},
pages = {1-5},
abstract = {Sensor and lens blur degrade images acquired by digital cameras. Simple and fast removal of blur using linear filtering, such as Wiener filter, produces results that are not acceptable in most of the cases due to ringing artifacts close to image borders and around edges in the image. More elaborate deconvolution methods with non-smooth regularization, such as total variation, provide superior performance with less artifacts, however at a price of increased computational cost. We consider the alternating directions method of multipliers, which is a popular choice to solve such non-smooth convex problems, and show that individual steps of the method can be decomposed to simple filtering and element-wise operations. Filtering is performed with two sets of filters, called restoration and update filters, which are learned for the given type of blur and noise level with two different learning methods. The proposed deconvolution algorithm is implemented in the spatial domain and can be easily extended to include other restoration tasks such as demosaicing and super-resolution. Experiments demonstrate performance of the algorithm with respect to the size of learned filters, number of iterations, noise level and type of blur.},
keywords = {convex programming;deconvolution;image filtering;image restoration;iterative methods;learning (artificial intelligence);Wiener filters;linear filtering;image borders;deconvolution methods;nonsmooth regularization;nonsmooth convex problems;alternating directions method of multipliers;element-wise operations;iterative Wiener filtering;lens;digital cameras;learned filter methods;ringing artifact suppression;image demosaicing;Image restoration;Convolution;Deconvolution;Signal processing algorithms;Optimization;Europe;Wiener filter;LMMSE;deconvolution;total variation;ADMM;non-smooth optimization},
doi = {10.23919/EUSIPCO.2019.8903114},
issn = {2076-1465},
month = {Sep.},
url = {https://www.eurasip.org/proceedings/eusipco/eusipco2019/proceedings/papers/1570531236.pdf},
}Downloads: 0
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Simple and fast removal of blur using linear filtering, such as Wiener filter, produces results that are not acceptable in most of the cases due to ringing artifacts close to image borders and around edges in the image. More elaborate deconvolution methods with non-smooth regularization, such as total variation, provide superior performance with less artifacts, however at a price of increased computational cost. We consider the alternating directions method of multipliers, which is a popular choice to solve such non-smooth convex problems, and show that individual steps of the method can be decomposed to simple filtering and element-wise operations. Filtering is performed with two sets of filters, called restoration and update filters, which are learned for the given type of blur and noise level with two different learning methods. 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