TY - JOUR
T1 - Sparsity-Based Edge Noise Removal from Bilevel Graphical Document Images
AU - Hoang, Thai V.
AU - Barney Smith, Elisa H.
AU - Tabbone, Salvatore
N1 - Hoang, Thai V.; Barney Smith, Elisa H.; and Tabbone, Salvatore. (2013). "Sparsity-Based Edge Noise Removal from Bilevel Graphical Document Images". International Journal on Document Analysis and Recognition, 17(2), 161-179. https://doi.org/10.1007/s10032-013-0213-4
PY - 2014/6/1
Y1 - 2014/6/1
N2 - This paper presents a new method to remove edge noise from graphical document images using geometrical regularities of the graphics contours that exist in the images. Denoising is understood as a recovery problem and is accomplished by employing a sparse representation framework in the form of a basis pursuit denoising algorithm. Directional information of the graphics contours is encoded by atoms in an overcomplete dictionary which is designed to match the input data. The optimal precision parameter used in this framework is shown to have a linear relationship with the level of the noise that exists in the image. Experimental results show the superiority of the proposed method over existing ones in terms of image recovery and contour raggedness.
AB - This paper presents a new method to remove edge noise from graphical document images using geometrical regularities of the graphics contours that exist in the images. Denoising is understood as a recovery problem and is accomplished by employing a sparse representation framework in the form of a basis pursuit denoising algorithm. Directional information of the graphics contours is encoded by atoms in an overcomplete dictionary which is designed to match the input data. The optimal precision parameter used in this framework is shown to have a linear relationship with the level of the noise that exists in the image. Experimental results show the superiority of the proposed method over existing ones in terms of image recovery and contour raggedness.
KW - bilevel image denoising
KW - dictionary learning
KW - directional denoising
KW - image degradation model
KW - noise spread
KW - sparse representation
UR - https://scholarworks.boisestate.edu/electrical_facpubs/253
M3 - Article
JO - International Journal on Document Analysis and Recognition
JF - International Journal on Document Analysis and Recognition
ER -