Efficient block noise removal based on nonlinear manifolds

Haoying Fu; Hongyuan Zha; Jesse Barlow

doi:10.1109/ICCV.2005.82

Efficient block noise removal based on nonlinear manifolds

Haoying Fu, Hongyuan Zha, Jesse Barlow

Computer Science and Engineering

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

Abstract

The problem of block noise removal is considered. It is assumed that the original image is on or close to a subspace of admissible images in the form of a low dimensional nonlinear manifold. We propose to use a close variant of the total variation regularizer for measuring block noise. Based on this noise measure, we present an effective approach that reconstructs the original image in the presence of block noise. Our main computational task is the solution of a quadratic programming problem, for which we propose a very efficient interior point method. The effectiveness and efficiency of our approach is demonstrated by an example.

Original language	English (US)
Title of host publication	Proceedings - 10th IEEE International Conference on Computer Vision, ICCV 2005
Pages	549-556
Number of pages	8
DOIs	https://doi.org/10.1109/ICCV.2005.82
State	Published - 2005
Event	Proceedings - 10th IEEE International Conference on Computer Vision, ICCV 2005 - Beijing, China Duration: Oct 17 2005 → Oct 20 2005

Publication series

Name	Proceedings of the IEEE International Conference on Computer Vision
Volume	I

Other

Other	Proceedings - 10th IEEE International Conference on Computer Vision, ICCV 2005
Country/Territory	China
City	Beijing
Period	10/17/05 → 10/20/05

All Science Journal Classification (ASJC) codes

Software
Computer Vision and Pattern Recognition

Access to Document

10.1109/ICCV.2005.82

Cite this

@inproceedings{5f13a01527dc438b94809391de94fc91,

title = "Efficient block noise removal based on nonlinear manifolds",

abstract = "The problem of block noise removal is considered. It is assumed that the original image is on or close to a subspace of admissible images in the form of a low dimensional nonlinear manifold. We propose to use a close variant of the total variation regularizer for measuring block noise. Based on this noise measure, we present an effective approach that reconstructs the original image in the presence of block noise. Our main computational task is the solution of a quadratic programming problem, for which we propose a very efficient interior point method. The effectiveness and efficiency of our approach is demonstrated by an example.",

author = "Haoying Fu and Hongyuan Zha and Jesse Barlow",

year = "2005",

doi = "10.1109/ICCV.2005.82",

language = "English (US)",

isbn = "076952334X",

series = "Proceedings of the IEEE International Conference on Computer Vision",

pages = "549--556",

booktitle = "Proceedings - 10th IEEE International Conference on Computer Vision, ICCV 2005",

note = "Proceedings - 10th IEEE International Conference on Computer Vision, ICCV 2005 ; Conference date: 17-10-2005 Through 20-10-2005",

}

Fu, H, Zha, H & Barlow, J 2005, Efficient block noise removal based on nonlinear manifolds. in Proceedings - 10th IEEE International Conference on Computer Vision, ICCV 2005., 1541302, Proceedings of the IEEE International Conference on Computer Vision, vol. I, pp. 549-556, Proceedings - 10th IEEE International Conference on Computer Vision, ICCV 2005, Beijing, China, 10/17/05. https://doi.org/10.1109/ICCV.2005.82

Efficient block noise removal based on nonlinear manifolds. / Fu, Haoying; Zha, Hongyuan; Barlow, Jesse.
Proceedings - 10th IEEE International Conference on Computer Vision, ICCV 2005. 2005. p. 549-556 1541302 (Proceedings of the IEEE International Conference on Computer Vision; Vol. I).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

TY - GEN

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AU - Zha, Hongyuan

AU - Barlow, Jesse

PY - 2005

Y1 - 2005

N2 - The problem of block noise removal is considered. It is assumed that the original image is on or close to a subspace of admissible images in the form of a low dimensional nonlinear manifold. We propose to use a close variant of the total variation regularizer for measuring block noise. Based on this noise measure, we present an effective approach that reconstructs the original image in the presence of block noise. Our main computational task is the solution of a quadratic programming problem, for which we propose a very efficient interior point method. The effectiveness and efficiency of our approach is demonstrated by an example.

AB - The problem of block noise removal is considered. It is assumed that the original image is on or close to a subspace of admissible images in the form of a low dimensional nonlinear manifold. We propose to use a close variant of the total variation regularizer for measuring block noise. Based on this noise measure, we present an effective approach that reconstructs the original image in the presence of block noise. Our main computational task is the solution of a quadratic programming problem, for which we propose a very efficient interior point method. The effectiveness and efficiency of our approach is demonstrated by an example.

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DO - 10.1109/ICCV.2005.82

M3 - Conference contribution

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T3 - Proceedings of the IEEE International Conference on Computer Vision

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BT - Proceedings - 10th IEEE International Conference on Computer Vision, ICCV 2005

T2 - Proceedings - 10th IEEE International Conference on Computer Vision, ICCV 2005

Y2 - 17 October 2005 through 20 October 2005

ER -

Efficient block noise removal based on nonlinear manifolds

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