Image de-noising by non-local means algorithm

Dixit, A. A.; Phadke, Anuradha C.

doi:10.1109/icsipr.2013.6497937

Cited by 8 publications

(3 citation statements)

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“…Finally, the property of each separated object is obtained for subsequent analysis. Since any coloured image or grey‐scale image can always be converted into binary image by some preprocessing techniques such as the image de‐noising method (Kushwaha et al ., ; Dixit & Phadke, ) and the image binaryzation process (Tsai & Lee, ; He et al ., ) and those techniques are not directly related to the proposed method, we do not give much detail on it in this paper. Like that was done in many previous works (Charles et al ., ; Sun & Luo, ), we consider only binary images with dark objects in bright background in this work.…”

Section: Adaptive Striping Watershed Algorithmmentioning

confidence: 99%

Adaptive striping watershed segmentation method for processing microscopic images of overlapping irregular‐shaped and multicentre particles

Xiao

Bai

et al. 2014

Journal of Microscopy

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Oversegmentation is a major drawback of the morphological watershed algorithm. Here, we study and reveal that the oversegmentation is not only because of the irregular shapes of the particle images, which people are familiar with, but also because of some particles, such as ellipses, with more than one centre. A new parameter, the striping level, is introduced and the criterion for striping parameter is built to help find the right markers prior to segmentation. An adaptive striping watershed algorithm is established by applying a procedure, called the marker searching algorithm, to find the markers, which can effectively suppress the oversegmentation. The effectiveness of the proposed method is validated by analysing some typical particle images including the images of gold nanorod ensembles.

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Section: Adaptive Striping Watershed Algorithmmentioning

confidence: 99%

Adaptive striping watershed segmentation method for processing microscopic images of overlapping irregular‐shaped and multicentre particles

Xiao

Bai

et al. 2014

Journal of Microscopy

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“…Enming Luo et al [11] incorporated noise dependence into the weight function of NL-means and propose two process stages providing better denoising effect. Dixit and Phadke [12,13] proposed a local adaptive filtering method that can track the noise variance during the NL-means estimation process and after applying a local denoising filtering method in those particular areas with remaining noise.…”

Section: Introductionmentioning

confidence: 99%

Improved preclassification non local-means (IPNLM) for filtering of grayscale images degraded with additive white Gaussian noise

Hernández-Gutiérrez

Gallegos-Funes

Rosales

2018

J Image Video Proc.

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In this paper, we develop an extensive research on different types of grayscale images applying standard non local (NL)-means algorithm on different search and patch windows sizes to obtain optimal parameters where the values of criteria peak signal-to-noise ratio (PSNR), mean absolute error (MAE), and structural similarity index (SSIM) would be the best possible. The research shows quantitatively the importance on the appropriate selection of the windows sizes used during the filtering process. Based on the optimal parameters of the standard NL-means, we propose the improved preclassification non local-means (IPNLM) for filtering grayscale images degraded with additive white Gaussian noise (AWGN). The proposal uses a descriptors evaluation for each search window in the noisy image to apply statistical neighborhood preclassification respect to the homogeneity of each window to distinguish whether the current noisy pixel is in a homogeneous region or it is in an edge object region. Also, two thresholds based on the standard deviation of the local region in the noisy image are proposed to classify the pixels and perform a filtering level degree providing a commitment between the image denoising and the processing time. The proposal IPNLM reveals good results outperforming other filters based on NL-means by balancing the tradeoff between the noise suppression, detail preservation, and processing time. Experimental results demonstrate that IPNLM algorithm can reduce considerably the processing time from 8 through 15 times in comparison with the standard NL-means and other analyzed filters.

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“…In the original NLM method, the restored gray value of each pixel is obtained by the weighted average of the gray values of all pixels in the image, and the weights of pixels is calculated based on the similarity between the neighborhood of the current pixel and the neighborhood corresponding to the other pixels [3][4][5][6]. To reduce the cost of computational complexity, the modified NLM schemes process the weighted average considering a limited area around the current pixel instead of the entire image [7]; even so, they cannot yet be extensively used in real-time systems for the excessive complexity. In this paper, a fast NLM denoising algorithm is proposed.…”

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A Patch-Based Non-local Means Denoising Method Using Hierarchical Searching

Dou

Han

Sheng

et al. 2015

Communications in Computer and Information Science

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Abstract. Non-local means (NLM) is a powerful denoising algorithm that can protect texture effectively. However, the computational complexity of this method is so high that it is difficult to be widely applied in real-time systems. In this paper, we propose a fast NLM denoising algorithm which can product comparable or better result with less computation time than the traditional NLM methods. Some experimental results are provided to demonstrate the superiority of the proposed method.Keywords: Non-local means · Patch-based processing · Hierarchical searching IntroductionImage and video denoising is one of the most important and challenging problem in the image processing community that has been studied for many years and continues to attract researchers with an aim to perform better restoration in the presence of noise. Many denoising algorithms have been proposed, such as median filtering, adaptive wiener filtering, wavelet denoising [1,2], etc. However, for various reasons, these methods cannot protect texture very well; an excellent denoising algorithm is required. In recent years, because the highly-structured geometrical form of natural images was taken into account, the nonlocal means (NLM) algorithm and some developed versions have been presented. NLM algorithms can achieve results which are close to the state-of-the-art. In the original NLM method, the restored gray value of each pixel is obtained by the weighted average of the gray values of all pixels in the image, and the weights of pixels is calculated based on the similarity between the neighborhood of the current pixel and the neighborhood corresponding to the other pixels [3][4][5][6]. To reduce the cost of computational complexity, the modified NLM schemes process the weighted average considering a limited area around the current pixel instead of the entire image [7]; even so, they cannot yet be extensively used in real-time systems for the excessive complexity. In this paper, a fast NLM denoising algorithm is proposed. Unlike the previous methods to restore image by weighted average of each pixel, we process image based on patch. For each patch, we find the similar area by fast scan, note that the similar area is always discontinuous and irregular geometrically; and then we restore the current patch by weighted average of each patch in the similar area.

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Image de-noising by non-local means algorithm

Cited by 8 publications

References 0 publications

Adaptive striping watershed segmentation method for processing microscopic images of overlapping irregular‐shaped and multicentre particles

Adaptive striping watershed segmentation method for processing microscopic images of overlapping irregular‐shaped and multicentre particles

Improved preclassification non local-means (IPNLM) for filtering of grayscale images degraded with additive white Gaussian noise

A Patch-Based Non-local Means Denoising Method Using Hierarchical Searching

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