Beyond pixels and regions: A non-local patch means (NLPM) method for content-level restoration, enhancement, and reconstruction of degraded document images

A patch-based non-local restoration and reconstruction method for preprocessing degraded document images is introduced. The method collects relative data from the whole input image, while the image data are first represented by a content-level descriptor based on patches. This patch-equivalent representation of the input image is then corrected based on similar patches identified using a modified genetic algorithm (GA) resulting in a low computational load. The corrected patch-equivalent is then converted to the output restored image. The fact that the method uses the patches at the content level allows it to incorporate high-level restoration in an objective and self-sufficient way. The method has been applied to several degraded document images, including the DIBCO'09 contest dataset with promising results.

[1]  Anil C. Kokaram,et al.  Bayesian Example Based Segmentation using a Hybrid Energy Model , 2007, 2007 IEEE International Conference on Image Processing.

[2]  Jitendra Malik,et al.  Scale-Space and Edge Detection Using Anisotropic Diffusion , 1990, IEEE Trans. Pattern Anal. Mach. Intell..

[3]  C. V. Jawahar,et al.  Contextual restoration of severely degraded document images , 2009, 2009 IEEE Conference on Computer Vision and Pattern Recognition.

[4]  Mohamed Cheriet,et al.  A multi-scale framework for adaptive binarization of degraded document images , 2010, Pattern Recognit..

[5]  Tolga Tasdizen.  Principal components for non-local means image denoising , 2008, 2008 15th IEEE International Conference on Image Processing.

[6]  H. M. Salinas,et al.  Comparison of PDE-Based Nonlinear Diffusion Approaches for Image Enhancement and Denoising in Optical Coherence Tomography , 2007, IEEE Transactions on Medical Imaging.

[7]  B. Silverman,et al.  The Stationary Wavelet Transform and some Statistical Applications , 1995 .

[8]  Richard Baraniuk,et al.  The Dual-tree Complex Wavelet Transform , 2007 .

[9]  Karl Pearson F.R.S..  LIII. On lines and planes of closest fit to systems of points in space , 1901 .

[10]  Mohamed Cheriet,et al.  RSLDI: Restoration of single-sided low-quality document images , 2009, Pattern Recognit..

[11]  Dorothea Heiss-Czedik,et al.  An Introduction to Genetic Algorithms. , 1997, Artificial Life.

[12]  Thomas S. Huang,et al.  Restoration and recognition in a loop , 2005, 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'05).

[13]  Jean-Michel Morel,et al.  A non-local algorithm for image denoising , 2005, 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'05).

[14]  Tolga Tasdizen,et al.  Principal Neighborhood Dictionaries for Nonlocal Means Image Denoising , 2009, IEEE Transactions on Image Processing.

[15]  Mohamed Cheriet,et al.  A Variational Approach to Degraded Document Enhancement , 2010, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[16]  Tony F. Chan,et al.  Image processing and analysis - variational, PDE, wavelet, and stochastic methods , 2005 .

[17]  Ioannis Pratikakis,et al.  ICDAR 2009 Document Image Binarization Contest (DIBCO 2009) , 2009, 2009 10th International Conference on Document Analysis and Recognition.

[18]  Christopher M. Bishop,et al.  Pattern Recognition and Machine Learning (Information Science and Statistics) , 2006 .

[19]  I. Daubechiesa,et al.  Variational image restoration by means of wavelets : Simultaneous decomposition , deblurring , and denoising , 2005 .

[20]  Venu Govindaraju,et al.  Preprocessing of Low-Quality Handwritten Documents Using Markov Random Fields , 2009, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[21]  Mohamed Cheriet,et al.  EFDM : Restoration of Single-sided Low-quality Document Images , 2008 .

[22]  Alessandro Foi,et al.  Image Denoising by Sparse 3-D Transform-Domain Collaborative Filtering , 2007, IEEE Transactions on Image Processing.

[23]  Elisa H. Barney Smith,et al.  Pre-Processing of Degraded Printed Documents by Non-local Means and Total Variation , 2009, 2009 10th International Conference on Document Analysis and Recognition.

[24]  Cyrus D. Cantrell,et al.  Modern Mathematical Methods for Physicists and Engineers , 2000 .

[25]  Guy Lapalme,et al.  A systematic analysis of performance measures for classification tasks , 2009, Inf. Process. Manag..

[26]  Its'hak Dinstein,et al.  Adaptive shape prior for recognition and variational segmentation of degraded historical characters , 2009, Pattern Recognit..

[27]  John B. Shoven,et al.  I , Edinburgh Medical and Surgical Journal.

[28]  Antonin Chambolle,et al.  Nonlinear wavelet image processing: variational problems, compression, and noise removal through wavelet shrinkage , 1998, IEEE Trans. Image Process..

[29]  Mohamed Cheriet,et al.  Robust NL-Means Filter With Optimal Pixel-Wise Smoothing Parameter for Statistical Image Denoising , 2009, IEEE Transactions on Signal Processing.

[30]  Derek Bradley,et al.  Adaptive Thresholding using the Integral Image , 2007, J. Graph. Tools.

[31]  Max Mignotte.  Nonparametric multiscale energy-based mode and its application in some imagery problems , 2004, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[32]  N. Kingsbury.  Complex Wavelets for Shift Invariant Analysis and Filtering of Signals , 2001 .

[33]  Matti Pietikäinen,et al.  Adaptive document image binarization , 2000, Pattern Recognit..

[34]  Ingrid Daubechies,et al.  Variational image restoration by means of wavelets: simultaneous decomposition , 2005 .

[35]  F. Voci,et al.  Estimating the gradient in the Perona-Malik equation , 2004, IEEE Signal Processing Magazine.

[36]  Thomas M. Breuel,et al.  Efficient implementation of local adaptive thresholding techniques using integral images , 2008, Electronic Imaging.

[37]  Thomas S. Huang,et al.  Models for Patch-Based Image Restoration , 2006, 2006 Conference on Computer Vision and Pattern Recognition Workshop (CVPRW'06).

[38]  R. F. Moghaddam,et al.  Low quality document image modeling and enhancement , 2009, International Journal of Document Analysis and Recognition (IJDAR).

[39]  L. Rudin,et al.  Nonlinear total variation based noise removal algorithms , 1992 .

[40]  Abdelaziz Abid.  ‘Memory of the World’: Preserving Our Documentary Heritage , 1997 .

[41]  Ivan W. Selesnick.  A new complex-directional wavelet transform and its application to image denoising , 2002, Proceedings. International Conference on Image Processing.

[42]  Ian T. Young,et al.  Fundamentals of Image Processing , 1998 .

[43]  Y. Yoshitomi,et al.  A method for expressing human posture as 3DCG using thermal image processing and 3D model fitting , 2009, Artificial Life and Robotics.