A Chinese character structure preserved denoising method for Chinese tablet calligraphy document images based on KSVD dictionary learning

As an art form, Chinese ancient calligraphy tablet works occupy an important position in the heritage of Chinese culture. However, because of natural or man-made decay, there appear lots of noises in these ancient tablet works images, which have an important effect on the quality of the tablet images. To address this problem, a character structure preserved denoising method based on KSVD dictionary learning was proposed in this paper. This new proposed method consists of two major operations: dividing-frequency denoising and ant-like noises removal in binary image. At the stage of dividing-frequency denosing, the Butterworth low pass filter was employed to filter and extract low frequency part of the images firstly. Then, KSVD dictionary learning algorithm was used for smoothing the high frequency image and extracting image edges and the extracted edge images was then fused with the denoised low frequency part of the images. At the stage of ant-like noises removal, the fused image is converted into a binary one firstly. Then, the connected region method is employed to remove isolated ant-like noise; ergodic method is used to fill holes of strokes. Finally strokes thorn was eliminated by using the median filter. Experimental results demonstrate that the proposed method can effectively remove most image noise (including various block noise, linear noise and ant-like noise) and preserve characters better than existing methods.