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

Shi, Zhiping; Xu, Binxin; Zheng, Xiaojiao; Zhao, Minghua

doi:10.1007/s11042-016-4284-3

Cited by 15 publications

(14 citation statements)

References 15 publications

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“…In Fig. 10, we compare the proposed method in this paper with our two prior works, including an integrated de-noising method [11] and the KSVD de-noising method [12]. It is observed that the three methods were able to take out of most image noise from the input noisy images, whereas it should be noticed that the proposed method in this paper obtains the best results with clearer images.…”

Section: Qualitative Resultsmentioning

confidence: 91%

“…In [11], an integration of multiple de-noising filters was proposed for removing block and line noise from stele images. In [12], KSVD dictionary learning was used for de-noising as well as for preserving the character structures.…”

Section: Related Workmentioning

confidence: 99%

“…We also generated 50 synthetically noised stele images using Adobe Photoshop CS6 to quantitatively evaluate the proposed method. For comparison, several state of the art image de-noising methods, including Zheng's method [1], Zhang's method [10], L0 gradient minimization smoothing [13], and block-matching 3D filtering (BM3D) [17], as well as methods in our two prior works [11,12] are employed in our experiments. The results of these methods are produced in MatLab with authors suggested parameter settings.…”

Section: Experimental Settingmentioning

confidence: 99%

“…Referred to [18], the definition formulation of PSNR and SSIM are described as Eq. (11,12), respectively.…”

Section: Quantitative Evaluationmentioning

confidence: 99%

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A de-noising method based on L0 gradient minimization and guided filter for ancient Chinese calligraphy works on steles

2019

J Image Video Proc.

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A clear stele image of ancient Chinese calligraphy pieces is very useful for studying ancient Chinese calligraphy. However, due to hundreds of or even thousands of years of natural or artificial damage on stele, images of ancient Chinese stele calligraphy works usually suffer from a large amount of image noise, and which usually leads to a poor visibility. To address this problem, in this paper, we propose a de-noising method based on L0 gradient minimization and guided filter. It consists of two main operations in sequence: First, L0 gradient minimization is utilized to obtain a random-noise free map, and then the random-noise free map is used as a guided image, and convoluted with its corresponding original noised stele image by a guided filter to obtain an edge preserved random-noise free image. Finally, the eight-connection region-based de-noising technique is followed to remove ant-like isolated blocks. Experiments demonstrate that the proposed method is superior to several recent published stele image de-noising techniques in terms of preserving the character structures.

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Section: Qualitative Resultsmentioning

confidence: 91%

Section: Related Workmentioning

confidence: 99%

Section: Experimental Settingmentioning

confidence: 99%

“…Referred to [18], the definition formulation of PSNR and SSIM are described as Eq. (11,12), respectively.…”

Section: Quantitative Evaluationmentioning

confidence: 99%

See 2 more Smart Citations

A de-noising method based on L0 gradient minimization and guided filter for ancient Chinese calligraphy works on steles

2019

J Image Video Proc.

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“…The framework can automatically determine the undesirable random noisy components from true image components directly from a noisy image. Dictionary learning algorithm was adapted to filter Chinese character images by Shi et al [54]. They divided the image frequency to low and high frequencies.…”

Section: Patch Construction Stepmentioning

confidence: 99%

Patch-based models and algorithms for image denoising: a comparative review between patch-based images denoising methods for additive noise reduction

Alkinani

El-Sakka

2017

J Image Video Proc.

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Background: Digital images are captured using sensors during the data acquisition phase, where they are often contaminated by noise (an undesired random signal). Such noise can also be produced during transmission or by poor-quality lossy image compression. Reducing the noise and enhancing the images are considered the central process to all other digital image processing tasks. The improvement in the performance of image denoising methods would contribute greatly on the results of other image processing techniques. Patch-based denoising methods recently have merged as the state-of-the-art denoising approaches for various additive noise levels. In this work, the use of the state-of-the-art patch-based denoising methods for additive noise reduction is investigated. Various types of image datasets are addressed to conduct this study. Methods: We first explain the type of noise in digital images and discuss various image denoising approaches, with a focus on patch-based denoising methods. Then, we experimentally evaluate both quantitatively and qualitatively the patch-based denoising methods. The patch-based image denoising methods are analyzed in terms of quality and computational time. Results: Despite the sophistication of patch-based image denoising approaches, most patch-based image denoising methods outperform the rest. Fast patch similarity measurements produce fast patch-based image denoising methods. Conclusion: Patch-based image denoising approaches can effectively reduce noise and enhance images. Patch-based image denoising approach is the state-of-the-art image denoising approach.Keywords: Patch-based image denoising, Bilateral filter, Non-local means filtering, Probabilistic patch-based filtering, Dictionary learning filtering, K-SVD, Gaussian patch-PCA filtering, BM3D Review IntroductionThe noise level in digital images may vary from being almost imperceptible to being very noticeable. Image denoising techniques attempt to produce a new image that has less noise, i.e., closer to the original noise-free image. Image denoising techniques can be grouped into two main approaches: pixel-based image filtering and patch-based *Correspondence: elsakka@csd.uwo.ca 2 Department of Computer Science, Middlesex College, Western University, 1151 Richmond Street, N6A 5B7, London, Ontario, Canada Full list of author information is available at the end of the article image filtering. A pixel-based image filtering scheme is mainly a proximity operation used for manipulating one pixel at a time (pixel-wise) based on its spatial neighboring pixels located within a kernel. On the other hand, in patch-based image filtering, the noisy image is divided into patches, or "blocks, " which are then manipulated separately in order to provide an estimate of the true pixel values (patch-wise) based on similar patches located within a search window. This approach utilizes the redundancy and the similarity among the various parts of the input image. Figure 1 shows the mechanism of the two approaches.

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A GAN-based Denoising Method for Chinese Stele and Rubbing Calligraphic Image

Wang

Zhang

et al. 2022

Vis Comput

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

Cited by 15 publications

References 15 publications

A de-noising method based on L0 gradient minimization and guided filter for ancient Chinese calligraphy works on steles

A de-noising method based on L0 gradient minimization and guided filter for ancient Chinese calligraphy works on steles

Patch-based models and algorithms for image denoising: a comparative review between patch-based images denoising methods for additive noise reduction

A GAN-based Denoising Method for Chinese Stele and Rubbing Calligraphic Image

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