A novel Non-local means image denoising method based on grey theory

Liu, Hongjun; Suen, Ching Y.

doi:10.1016/j.patcog.2015.05.028

Cited by 82 publications

(39 citation statements)

References 27 publications

(37 reference statements)

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“…To quantitative evaluated the performance of the proposed method, the proposed method was evaluated in the terms of PSNR (Peak Signal to Noise Ratio) [7] and SSIM (structural similarity index measurement) [18] by comparing with mean filter, medium filter and Gauss filter. The ground-truth images used in this study are man-made images by experienced calligraphers, examples are shown in Fig.…”

Section: Objective Evaluationmentioning

confidence: 99%

An integrated method for ancient Chinese tablet images de-noising based on assemble of multiple image smoothing filters

Shi

Zheng

et al. 2016

Multimed Tools Appl

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There are unavoidably lots of noises in tablet images due to natural or man-made decay, which have a significant affect on learning and studying of the ancient Chinese calligraphy works with Chinese tablet images. To address this problem, an integrated denoising method, based on assemble of multiple image smoothing filters, is proposed in this paper. To avoid damaging characters and losing detail information, input Chinese tablet images are enhanced by the Guided filter and multi-scale Retinex filter firstly. Then the enhanced tablet images are converted to binary ones by the Otsu thresholding filter. Finally, most random and block noises are removed using an improved scan-length statistics filter based on connected region. The performance of the proposed method was validated on our Chinese tablet image data set, which consists of 200 Chinese tablet images with different kinds of noise. Experiments show 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.

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Section: Objective Evaluationmentioning

confidence: 99%

An integrated method for ancient Chinese tablet images de-noising based on assemble of multiple image smoothing filters

Shi

Zheng

et al. 2016

Multimed Tools Appl

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“…Interests regarding image preprocessing, including image de-noising [5] and image enhancement [6], especially on ancient Chinese calligraphy image enhancement [1][2][3][4][7][8][9][10] have seen increasing in recent years; for instance, Zheng et.al. [1] presented a de-noising method for stele images using guided filter on the L channel.…”

Section: Related Workmentioning

confidence: 99%

“…However, to the best of our knowledge, though there are many works on image de-noising [2][3][4], few works have focused on ancient Chinese calligraphy images. For this reason, a de-noising method for ancient Chinese calligraphy works on steles based on L0 gradient minimization and guided filter is proposed in this paper.…”

Section: Introductionmentioning

confidence: 99%

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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“…Note that the tuning parameter, σ 2 y , is often set close to the noise variance. Studies of filter parameter selection can be found in [3,[19][20][21].…”

Section: Single-frame Non-local Means Filtermentioning

confidence: 99%

Recursive non-local means filter for video denoising

Ali

Hardie

2017

J Image Video Proc.

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In this paper, we propose a computationally efficient algorithm for video denoising that exploits temporal and spatial redundancy. The proposed method is based on non-local means (NLM). NLM methods have been applied successfully in various image denoising applications. In the single-frame NLM method, each output pixel is formed as a weighted sum of the center pixels of neighboring patches, within a given search window. The weights are based on the patch intensity vector distances. The process requires computing vector distances for all of the patches in the search window. Direct extension of this method from 2D to 3D, for video processing, can be computationally demanding. Note that the size of a 3D search window is the size of the 2D search window multiplied by the number of frames being used to form the output. Exploiting a large number of frames in this manner can be prohibitive for real-time video processing. Here, we propose a novel recursive NLM (RNLM) algorithm for video processing. Our RNLM method takes advantage of recursion for computational savings, compared with the direct 3D NLM. However, like the 3D NLM, our method is still able to exploit both spatial and temporal redundancy for improved performance, compared with 2D NLM. In our approach, the first frame is processed with single-frame NLM. Subsequent frames are estimated using a weighted sum of pixels from the current frame and a pixel from the previous frame estimate. Only the single best matching patch from the previous estimate is incorporated into the current estimate. Several experimental results are presented here to demonstrate the efficacy of our proposed method in terms of quantitative and subjective image quality.

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A novel Non-local means image denoising method based on grey theory

Cited by 82 publications

References 27 publications

An integrated method for ancient Chinese tablet images de-noising based on assemble of multiple image smoothing filters

An integrated method for ancient Chinese tablet images de-noising based on assemble of multiple image smoothing filters

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

Recursive non-local means filter for video denoising

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