Evaluation of Current Documents Image Denoising Techniques: A Comparative Study

The research to come out with effective and noise-free images is still fresh despite several years’ efforts. Even though, recently, several methods have been proposed, each approach has its own merits and limitations. Moreover, outstanding performances are exhibited for the tailored applications but fail in general and create several flaws and blur images of fine structures. The purpose of this study is to compare achievements thus far in the area of nonnatural documents image denoising approaches. Accordingly, existing techniques are organized into nonhomogeneous categories and detailed comparison is exhibited with examples. Finally, remaining problems and possible future directions in the domain of document images denoising are suggested.

[1]  Jiliu Zhou,et al.  An Efficient Salt-and-Pepper Noise Removal on Local Edge-Preserving Function , 2008, 2008 International Conference on Embedded Software and Systems Symposia.

[2]  Jean-Michel Morel,et al.  A Review of Image Denoising Algorithms, with a New One , 2005, Multiscale Model. Simul..

[3]  D. Narmadha,et al.  A Survey on Image Denoising Techniques , 2012 .

[4]  Ghazali Sulong,et al.  Simple and effective techniques for core-region detection and slant correction in offline script recognition , 2009, 2009 IEEE International Conference on Signal and Image Processing Applications.

[5]  Aysin Ertüzün,et al.  Applications of multiwavelet techniques to image denoising , 2002, Proceedings. International Conference on Image Processing.

[6]  Amjad Rehman,et al.  Methods and strategies on off-line cursive touched characters segmentation: a directional review , 2014, Artificial Intelligence Review.

[7]  Amjad Rehman,et al.  Effects of artificially intelligent tools on pattern recognition , 2013, Int. J. Mach. Learn. Cybern..

[8]  Michael L. Lightstone,et al.  A new efficient approach for the removal of impulse noise from highly corrupted images , 1996, IEEE Trans. Image Process..

[9]  Ghazali Sulong,et al.  Retraction Note: Document image analysis: issues, comparison of methods and remaining problems , 2014, Artif. Intell. Rev..

[10]  Amjad Rehman,et al.  Annotated comparisons of proposed preprocessing techniques for script recognition , 2014, Neural Computing and Applications.

[11]  C. Zhou,et al.  Comparisons of discrete wavelet transform, wavelet packet transform and stationary wavelet transform in denoising PD measurement data , 2006, Conference Record of the 2006 IEEE International Symposium on Electrical Insulation.

[12]  Akansha Mehrotra,et al.  A Novel Algorithm for Impulse Noise Removal and Edge Detection , 2012 .

[13]  Abdullah Zawawi Talib,et al.  Removing salt-and-pepper noise from binary images of engineering drawings , 2008, 2008 19th International Conference on Pattern Recognition.

[14]  Mahmoud R. El-Sakka,et al.  Novel Adaptive Filtering for Salt-and-Pepper Noise Removal from Binary Document Images , 2004, ICIAR.

[15]  Ghazali Sulong,et al.  An intelligent approach to image denoising , 2010 .

[16]  Zhou Wang,et al.  Progressive switching median filter for the removal of impulse noise from highly corrupted images , 1999 .

[17]  Guillermo Sapiro,et al.  Fast image and video denoising via nonlocal means of similar neighborhoods , 2005, IEEE Signal Processing Letters.

[18]  Alan C. Bovik,et al.  Streaking in median filtered images , 1987, IEEE Trans. Acoust. Speech Signal Process..

[19]  Jamshid Shanbehzadeh,et al.  A Hybrid Edge Detection Algorithm for Salt- and-Pepper Noise , 2011 .

[20]  Shuqun Zhang,et al.  A new impulse detector for switching median filters , 2002, IEEE Signal Processing Letters.

[21]  Amjad Rehman,et al.  Neural networks for document image preprocessing: state of the art , 2014, Artificial Intelligence Review.

[22]  Yan Guo-ping,et al.  Image Denoise Based on Soft-Threshold and Edge Enhancement , 2007, Second Workshop on Digital Media and its Application in Museum & Heritages (DMAMH 2007).

[23]  Dzulkifli Mohammad.  Digital watermarking for images security using discrete slantlet transform , 2014 .

[24]  Mila Nikolova,et al.  Regularizing Flows for Constrained Matrix-Valued Images , 2004, Journal of Mathematical Imaging and Vision.

[25]  H. Seo,et al.  A Comparison of Some State of the Art Image Denoising Methods , 2007, 2007 Conference Record of the Forty-First Asilomar Conference on Signals, Systems and Computers.

[26]  Richard G. Baraniuk,et al.  Multiple wavelet basis image denoising using Besov ball projections , 2004, IEEE Signal Processing Letters.

[27]  Amjad Rehman,et al.  DOCUMENT SKEW ESTIMATION AND CORRECTION: ANALYSIS OF TECHNIQUES, COMMON PROBLEMS AND POSSIBLE SOLUTIONS , 2011, Appl. Artif. Intell..

[28]  Lixin Fan,et al.  Binarizing document image using coplanar prefilter , 2001, Proceedings of Sixth International Conference on Document Analysis and Recognition.

[29]  Amjad Rehman,et al.  An automatic approach for line detection and removal without smash-up characters , 2011 .

[30]  Zhang Ping,et al.  Text document filters using morphological and geometrical features of characters , 2000, WCC 2000 - ICSP 2000. 2000 5th International Conference on Signal Processing Proceedings. 16th World Computer Congress 2000.

[31]  Raymond H. Chan,et al.  Salt-and-pepper noise removal by median-type noise detectors and detail-preserving regularization , 2005, IEEE Transactions on Image Processing.

[32]  Mauro Barni,et al.  A quasi-Euclidean norm to speed up vector median filtering , 2000, IEEE Trans. Image Process..

[33]  M. A. Yousuf,et al.  A New Method to Remove Noise in Magnetic Resonance and Ultrasound Images , 2010 .

[34]  Shuenn-Shyang Wang,et al.  A new impulse detection and filtering method for removal of wide range impulse noises , 2009, Pattern Recognit..

[35]  Steven J. Simske,et al.  Image Denoising Through Support Vector Regression , 2007, 2007 IEEE International Conference on Image Processing.

[36]  Xue Shufang,et al.  An Efficient Salt-and-Pepper Noise Removal , 2006 .

[37]  Jiang Zhu,et al.  Removal of salt-and-pepper noise based on compressed sensing , 2010 .

[38]  RehmanAmjad,et al.  Neural networks for document image preprocessing , 2014 .

[39]  Qin Wu,et al.  A new adaptive weight algorithm for salt and pepper noise removal , 2011, 2011 International Conference on Consumer Electronics, Communications and Networks (CECNet).

[40]  A. Ben Hamza,et al.  Removing Noise and Preserving Details with Relaxed Median Filters , 1999, Journal of Mathematical Imaging and Vision.

[41]  S. Mashaly Ahmed,et al.  Speckle noise reduction in SAR images using adaptive morphological filter , 2010, 2010 10th International Conference on Intelligent Systems Design and Applications.

[42]  Tapas Kanungo,et al.  Morphological degradation models and their use in document image restoration , 2001, Proceedings 2001 International Conference on Image Processing (Cat. No.01CH37205).

[43]  James D. Johnston,et al.  Spatial noise shaping based on human visual sensitivity and its application to image coding , 2001, Proceedings 2001 International Conference on Image Processing (Cat. No.01CH37205).

[44]  Amjad Rehman,et al.  Performance analysis of character segmentation approach for cursive script recognition on benchmark database , 2011, Digit. Signal Process..