Restoration of stained old manuscripts via a hybrid wavelet and bilateral filtering system

Abstract The conservation and restoration of old stained manuscripts is an activity devoted to the preservation and protection of things of historical and personal significance made mainly from paper, parchment, and skin. We present in this paper a hybrid implementation for de-noising and restoration of old degraded and stained manuscripts. This implementation is based on the statistical dependence of the wavelet coefficients of type Ortho-normal Wavelet Thresholding Algorithm based on the principle of Stein’s Unbiased Risk-Estimate Linear Expansion of Thresholds (OWT SURE-LET) and the synergy with bilateral filtering. First, the non-biased quadratic risk Stein estimator is applied to de-noise images corrupted by white Gaussian noise. In a second step, an improved bilateral filter is introduced to smooth and eliminate unnecessary details with the advantage of preserving edges between image regions. Obtained results show the effectiveness of the proposed synergy compared to separated approaches both on gray scale images and stained old manuscript.

[1]  Alexei A. Efros,et al.  Fast bilateral filtering for the display of high-dynamic-range images , 2002 .

[2]  L. McMillan,et al.  Video enhancement using per-pixel virtual exposures , 2005, SIGGRAPH 2005.

[3]  Daniel Cohen-Or,et al.  Bilateral mesh denoising , 2003 .

[4]  Amel Benazza-Benyahia,et al.  Building robust wavelet estimators for multicomponent images using Stein's principle , 2005, IEEE Transactions on Image Processing.

[5]  Patrick L. Combettes,et al.  Constraint construction in convex set theoretic signal recovery via Stein's principle [image denoising example] , 2004, 2004 IEEE International Conference on Acoustics, Speech, and Signal Processing.

[6]  Amel Benazza-Benyahia,et al.  A Nonlinear Stein-Based Estimator for Multichannel Image Denoising , 2007, IEEE Transactions on Signal Processing.

[7]  Naif Alajlan.  A Novel Recursive Algorithm For Detail-Preserving Impulse Noise Removal , 2010, J. King Saud Univ. Comput. Inf. Sci..

[8]  I. Johnstone,et al.  Adapting to Unknown Smoothness via Wavelet Shrinkage , 1995 .

[9]  Richard Szeliski,et al.  Digital photography with flash and no-flash image pairs , 2004, ACM Trans. Graph..

[10]  Thierry Blu,et al.  The SURE-LET Approach to Image Denoising , 2007, IEEE Transactions on Image Processing.

[11]  Hui Cheng,et al.  Bilateral Filtering-Based Optical Flow Estimation with Occlusion Detection , 2006, ECCV.

[12]  Albert C. S. Chung,et al.  Trilateral filtering for biomedical images , 2004, 2004 2nd IEEE International Symposium on Biomedical Imaging: Nano to Macro (IEEE Cat No. 04EX821).

[13]  Mohamed Cheriet,et al.  Historical document image restoration using multispectral imaging system , 2013, Pattern Recognit..

[14]  Richard Szeliski,et al.  Noise Estimation from a Single Image , 2006, 2006 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'06).

[15]  Thierry Blu,et al.  SURE-LET Multichannel Image Denoising: Interscale Orthonormal Wavelet Thresholding , 2008, IEEE Transactions on Image Processing.

[16]  Jörg Weule,et al.  Non-Linear Gaussian Filters Performing Edge Preserving Diffusion , 1995, DAGM-Symposium.

[17]  D. Leporini,et al.  A new wavelet estimator for image denoising , 1997 .

[18]  Frédo Durand,et al.  Flash photography enhancement via intrinsic relighting , 2004, SIGGRAPH 2004.

[19]  Roberto Manduchi,et al.  Bilateral filtering for gray and color images , 1998, Sixth International Conference on Computer Vision (IEEE Cat. No.98CH36271).

[20]  Michael Elad,et al.  On the origin of the bilateral filter and ways to improve it , 2002, IEEE Trans. Image Process..

[21]  Eero P. Simoncelli,et al.  Learning to be Bayesian without Supervision , 2006, NIPS.

[22]  Michael Elad,et al.  On the bilateral filter and ways to improve it , 2002 .

[23]  Frédo Durand,et al.  A gentle introduction to bilateral filtering and its applications , 2007, SIGGRAPH Courses.

[24]  Zhihong Li,et al.  Comparison of different image denoising algorithms for Chinese calligraphy images , 2016, Neurocomputing.