Comparison of different image denoising algorithms for Chinese calligraphy images
Introduction
A rubbing is a reproduction of the texture of a surface created by placing a piece of paper or similar material over the subject and then rubbing the paper with something to deposit marks, most commonly charcoal or pencil, but also various forms of blotted and rolled ink, chalk, wax, and many other substances as well [1]. Rubbing is a technique which invented in Wei, Jin, South and North Dynasties (220–589 AD), to make copies of inscribed records, using paper and ink. We can say that, rubbing is a unique way of documentation in China. It has made great contribution to the preservation of the Chinese culture, which is of high historical and aesthetic value and used in many ways in today׳s scientific research, and whose aesthetic feeling cannot be replaced by photographs. The use of rubbing was initially limited to making copies of stone inscriptions and then gradually expanded to bronze ware, jade ware, coins, ink stones, seals, tiles, wood ware, and even to oracle bones, objects dating from the Qin Dynasty (221–207 BCE) to the Ming Dynasty (1368–1644 CE).
The rubbings were made on ancient stone stelae and tomb tablets are not only important carriers of China ancient civilization but also are a classical template to research and learn the art of calligraphy. For the study of the history of writing and calligraphy, from the earliest script on shell and bone down to the running and cursive styles of later masters, inscriptions are irreplaceable sources. They have been tracing the evolution of writing, century after century, also.
Many Chinese ancient calligraphy work created by former famous calligraphers were carved on stone tablets, and calligraphy documents were produced by rubbing. Stone rubbings taken from them have been reproduced and reprinted widely and studied by generations of students, used models to learn and practice the art of calligraphy. Rubbings of engraved models of calligraphy, known as model writing (fa tie) are the most widely reproduced and consulted genre of rubbings in China, Japan, and Korea today.
In recent years, with the development of the scanning equipment and digital library technology, huge amount of rubbing from a stone inscription documents are scanned into the computer and stored as digital images, and have been made available to the general public through specialized web portals. Here are many Chinese rubbings sets often used by researchers. Such as, the East Asian Library׳s collection of Chinese rubbings in Berkeley University is second in number, outside of East Asia, only to that of the Field Museum of Natural History in Chicago. The Fine Arts Library currently houses 2602 individual East Asian rubbings, the majority of which are from China. Compared with paper rubbings, digital rubbings are more convenient for people to study.
Because the stones are not smooth originally and these inscriptions might have suffered from natural erosion during for thousands of years׳ storage or serious destruction when they are excavated and moved, most of them are not integrated and the original characters have been covered by many maculas, let alone the damage caused by having been tamped in the process of taking thousands of rubbings. Owing to the characteristics of stone inscriptions and the acquisition of objective factors of the environment, the original inscription images are filled with so much image noise that it may seriously affects the observation and research for inscription images, as shown in the two examples of rubbed calligraphy documents in Fig. 1, Fig. 2. In addition, there are other kinds of difficulties appearing in these images as different font types and sizes in the words, underlined and/or crossed-out words, etc. The combination of all these problems contributes to make the recognition process become very difficult, and hence, the preprocessing module quite essential.
The degradation of Chinese calligraphy images aesthetically affects the human perception and concomitantly the processes of feature recognition, segmentation, edge detection, etc. For correct interpretation of these images, restoration techniques are employed. Image denoising is one of the important fields in the restoration arena. The purpose of denoising is to obtain a good estimate of the original image from its degraded version and at the same time to preserve complex structures of images such as edges and textures. It is a difficult task to undertake, because the noises are randomly distributed in size and shape, and denoising sometimes may destroy simultaneously the characteristic parts of strokes simultaneously, such as the stroke tips and corners.
In the paper, we formulate this difficult rubbing image deblurring problem as an image denoising problem, using a pair of really rubbing images. The six classic denoising algorithm have been researched and compared, which are Anisotropic Diffusion filter, Wiener filter, TV(Total Variation, TV) minimizations, NLM (Non-Local Means, NLM), Bilateral filtering and Wavelet denoising. Finally, really rubbing images validate image denoising and analyzed the performance of the various algorithms have been described.
The article is organized as follows. Section 2 briefly describes the theory basis that different denoising methods and the relative researches are summarized. Five assessment parameters for image denoising have been explained in Section 3. Section 4 gives the experiments results of five different denoising methods, and five performance measurement criteria, such as PSNR, MSE, SNR, UQI and SSIM, are discussed. Section 5 gives a simple conclusion.
Section snippets
Relative works
In the late 1980s, the prevalence of fast computers, large computer memory, and inexpensive scanners fostered an increasing interest in document image analysis. An increasing number of Chinese calligraphy document images of different qualities are being scanned and archived. After document input by digital scanning, pixel processing is first performed (also called preprocessing and low-level processing in other literature) [10]. This level of processing includes operations that are applied to
Assessment parameters
The human eye is the only one able to decide if the quality of the image has been improved by the denoising method. To quantify the performance of the proposed algorithm, various parameters are employed such as PSNR, MSE, SNR, UQI and SSIM [11]. All the measures concerned are briefly introduced below.
- (1)
PSNR
PSNR (Peak Signal to Noise Ratio) is defined as the ratio of the variance of the noise-free signal to the mean squared error between the noise-free signal and the denoising signal [51]. PSNR is
Experimental results and discussions
To investigate the performance of the different denoising methods, the images which come from library of Harvard are chosen as input images with resolution of 410*454 pixels and 256 intensity levels. We now present numerical experimental results and comparisons to illustrate the valid performance of the different denoising algorithms. Fig. 4, Fig. 5, Fig. 6 show the original image and the output images processed after filtering processing.
For the Wiener filter using MATLAB׳s wiener2 function,
Conclusions
The text images obtained through rubbing were featured by many fuzzy details, bad effect and so on, so it might lose more details in the traditional handling process. Though there are many kinds of image denoising algorithm, we have compared our method to seven well-known and widely available denoising algorithms: Anisotropic Diffusion Filter, Wiener filter, TV (Total Variation), NLM (Non-Local Means, NLM), Bilateral filtering, hard VisuShrink thresholding, soft VisuShrink thresholding,
Acknowledgments
This work was supported by the National Natural Science Foundation of China (Grant no. 61472173), the Grants from the Science and Technology Planning Project of Jiangxi Province of China, No. 20111BBG70032-2.
Zhi-Kai Huang received the B.Sc. degree in Mechanical Manufacture and Automation from the Kunming Polytechnic University, Kunming, China, in 1990, the M.Sc. degree in Mechanical Engineering from Central Southern University, Changsha, China, in 2001. In 2006, he received Ph.D. degrees in Control Theory and Control Engineering from University Science and Technology of China (USTC). From 1990 to 1998, employed as an assistant to the Chief Engineer of the Tongling Nonferrous Metals Group Holdings
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Zhi-Kai Huang received the B.Sc. degree in Mechanical Manufacture and Automation from the Kunming Polytechnic University, Kunming, China, in 1990, the M.Sc. degree in Mechanical Engineering from Central Southern University, Changsha, China, in 2001. In 2006, he received Ph.D. degrees in Control Theory and Control Engineering from University Science and Technology of China (USTC). From 1990 to 1998, employed as an assistant to the Chief Engineer of the Tongling Nonferrous Metals Group Holdings Co.,Ltd, Anhui province, China. In 2007, he joined the Nanchang Institute of Technology (NIT). Currently, he is a Professor of College of Mechanical and Electrical Engineering in NIT, Nanchang, China. His research interests include pattern recognition, image processing and data mining.
Zhi-Hong Li received the B.Sc. and M.Sc. degree in Computer and Science Technology from Huazhong University of Science and Technology, Wuhan, China, in 1984 and 2004, respectively. Currently, she is a Professor of College of Mechanical and Electrical Engineering in NIT, Nanchang, China. His research interests include pattern recognition, Fault diagnosis of hydraulic generator sets.
Han Huang, he is currently studying the B.Sc. degree in Industrial Engineering at the Harbin Institute of Technology, Harbin, China. His research interests include industrial engineering, image processing.
Zhi-Biao Li received the B.Sc. degree in Mechanical Manufacture and Automation from Changchun University of Technology, Changchun, China, in 2003 and the M.Sc. degree in Mechanical Engineering from Nanchang University, Nanchang, China, in 2014. He started to teach in Nanchang Institute of Technology in 2003 and is currently a lecturer here.
Lin-Ying Hou received the B.Sc. degree in Computer and Science Technology from Nanchang Institute of Technology, Nanchang, China, in 2004. She joined the Computer Science & Technology Department of Nanchang Institute of Technology as a lecturer in 2007. Her research interests include image analysis, visual and geometric computing.