An Integrated De-noise and Enhancement Method for Ancient Chinese Tablet Images

Image noise can severely affect Chinese tablet image comprehension. In this paper, a novel integrated denoising and enhancement method for Chinese tablet image is proposed. The method consists of three stages de-noising operations. First, granular bright spots are smoothed by convoluting input Chinese tablet images with the bilateral filter. For obtaining more clear edge detail image, contrast enhancement between the foreground and background of the Chinese tablet image is followed by Top-Hat and Bottom-Hat transformations. Next, a mixture of run-length statistics and connected region techniques is employed to remove the random block noises in the images. Then, two mathematical morphological operators, erosion and dilation, are used to remove small holes and linear noises. Experimental results show that the proposed method can effectively remove most image noise (including block noise, and linear noise) and preserve characters better than existing methods.

[1]  Mark W. Schmidt,et al.  Minimizing finite sums with the stochastic average gradient , 2013, Mathematical Programming.

[2]  Ju Shen,et al.  Layer Depth Denoising and Completion for Structured-Light RGB-D Cameras , 2013, 2013 IEEE Conference on Computer Vision and Pattern Recognition.

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

[4]  Turgay Çelik,et al.  Two-dimensional histogram equalization and contrast enhancement , 2012, Pattern Recognit..

[5]  Zhang Jun-song,et al.  Denoising of Chinese calligraphy tablet images based on run-length statistics and structure characteristic of character strokes * , 2006 .

[6]  Zia-ur Rahman,et al.  A multiscale retinex for bridging the gap between color images and the human observation of scenes , 1997, IEEE Trans. Image Process..

[7]  Din-Chang Tseng,et al.  A novel stroke-based feature extraction for handwritten Chinese character recognition , 1999, Pattern Recognit..

[8]  W. Marsden I and J , 2012 .

[9]  X. Liu,et al.  An Optional Gauss Filter Image Denoising Method Based on Difference Image Fast Fuzzy Clustering , 2013 .

[10]  David S. Doermann,et al.  Stroke-Like Pattern Noise Removal in Binary Document Images , 2011, 2011 International Conference on Document Analysis and Recognition.

[11]  N. Otsu A threshold selection method from gray level histograms , 1979 .

[12]  Jianyu Yang,et al.  Adaptive Iterative Truncated Arithmetic Mean Filter in Image Denoising , 2012, 2012 IEEE 12th International Conference on Computer and Information Technology.

[13]  Hsi-Jian Lee,et al.  Dual-binarization and anisotropic diffusion of Chinese characters in calligraphy documents , 2001, Proceedings of Sixth International Conference on Document Analysis and Recognition.

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

[15]  Konstantinos Konstantinides,et al.  Noise estimation and filtering using block-based singular value decomposition , 1997, IEEE Trans. Image Process..