Edge Noise Removal in Bilevel Graphical Document Images Using Sparse Representation

Thai V. Hoang; Elisa Barney Smith; Salvatore Tabbone

Edge Noise Removal in Bilevel Graphical Document Images Using Sparse Representation

Thai V. Hoang, Elisa Barney Smith, Salvatore Tabbone

Electrical Engineering Department

Université Nancy 2

Research output: Contribution to journal › Article › peer-review

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Abstract

A new parametric method for edge noise removal in graphical document images is presented using geometrical regularities of the graphics contours that exists in the images. Denoising is understood as a recovery problem and is done by employing a sparse representation framework with a basis pursuit denoising algorithm for denoising and curvelet frames for encoding directional information of the graphics contours. The optimal precision parameter used in this framework is shown to have linear relationship with the level of the noise. Experimental results show the superiority of the proposed method over existing ones in terms of image recovery and contour raggedness.

Original language	American English
Journal	2011 18th IEEE International Conference on Image Processing
State	Published - 1 Jan 2011

Keywords

basis pursuit denoising
curvelet transform
edge noise removal
noise spread
sparse representation

EGS Disciplines

Electrical and Computer Engineering

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Edge Noise Removal in Bilevel Graphical Document Images Using SpaFinal published version, 1.71 MBLicense: Unspecified

http://dx.doi.org/10.1109/ICIP.2011.6116482

Cite this

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title = "Edge Noise Removal in Bilevel Graphical Document Images Using Sparse Representation",

abstract = " A new parametric method for edge noise removal in graphical document images is presented using geometrical regularities of the graphics contours that exists in the images. Denoising is understood as a recovery problem and is done by employing a sparse representation framework with a basis pursuit denoising algorithm for denoising and curvelet frames for encoding directional information of the graphics contours. The optimal precision parameter used in this framework is shown to have linear relationship with the level of the noise. Experimental results show the superiority of the proposed method over existing ones in terms of image recovery and contour raggedness.",

keywords = "basis pursuit denoising, curvelet transform, edge noise removal, noise spread, sparse representation",

author = "Hoang, {Thai V.} and {Barney Smith}, Elisa and Salvatore Tabbone",

note = "Hoang, Thai V.; Barney Smith, Elisa; and Tabbone, Salvatore. (2011). {"}Edge Noise Removal in Bilevel Graphical Document Images Using Sparse Representation{"}. In 2011 18th IEEE International Conference on Image Processing (ICIP) (pp. 3549-3552). IEEE. https://doi.org/10.1109/ICIP.2011.6116482",

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language = "American English",

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TY - JOUR

T1 - Edge Noise Removal in Bilevel Graphical Document Images Using Sparse Representation

AU - Hoang, Thai V.

AU - Barney Smith, Elisa

AU - Tabbone, Salvatore

N1 - Hoang, Thai V.; Barney Smith, Elisa; and Tabbone, Salvatore. (2011). "Edge Noise Removal in Bilevel Graphical Document Images Using Sparse Representation". In 2011 18th IEEE International Conference on Image Processing (ICIP) (pp. 3549-3552). IEEE. https://doi.org/10.1109/ICIP.2011.6116482

PY - 2011/1/1

Y1 - 2011/1/1

N2 - A new parametric method for edge noise removal in graphical document images is presented using geometrical regularities of the graphics contours that exists in the images. Denoising is understood as a recovery problem and is done by employing a sparse representation framework with a basis pursuit denoising algorithm for denoising and curvelet frames for encoding directional information of the graphics contours. The optimal precision parameter used in this framework is shown to have linear relationship with the level of the noise. Experimental results show the superiority of the proposed method over existing ones in terms of image recovery and contour raggedness.

AB - A new parametric method for edge noise removal in graphical document images is presented using geometrical regularities of the graphics contours that exists in the images. Denoising is understood as a recovery problem and is done by employing a sparse representation framework with a basis pursuit denoising algorithm for denoising and curvelet frames for encoding directional information of the graphics contours. The optimal precision parameter used in this framework is shown to have linear relationship with the level of the noise. Experimental results show the superiority of the proposed method over existing ones in terms of image recovery and contour raggedness.

KW - basis pursuit denoising

KW - curvelet transform

KW - edge noise removal

KW - noise spread

KW - sparse representation

UR - https://scholarworks.boisestate.edu/electrical_facpubs/186

M3 - Article

JO - 2011 18th IEEE International Conference on Image Processing

JF - 2011 18th IEEE International Conference on Image Processing

ER -

Edge Noise Removal in Bilevel Graphical Document Images Using Sparse Representation

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Keywords

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