Relevant bibliographies by topics / Non-local denoising filter

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Academic literature on the topic 'Non-local denoising filter'

Author: Grafiati
Published: 28 June 2021
Last updated: 17 February 2022

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Journal articles on the topic "Non-local denoising filter"

1

NamAnh, Dao. "Image Denoising by Addaptive Non-Local Bilatetal Filter." International Journal of Computer Applications 99, no. 12 (2014): 4–10. http://dx.doi.org/10.5120/17423-8275.

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2

Choudhary, Nidhi, Anant Singh, and Siddharth Srivastava. "Image Denoising using Improved Non-Local Means Filter." Journal of Electronic Design Engineering 6, no. 2 (2020): 15–18. http://dx.doi.org/10.46610/joede.2020.v06i02.003.

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3

Judson, Matt, Troy Viger, and Hyeona Lim. "Efficient and Robust Non-Local Means Denoising Methods for Biomedical Images." ITM Web of Conferences 29 (2019): 01003. http://dx.doi.org/10.1051/itmconf/20192901003.

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Denoising is an important step to improve image quality and to increase the performance of image analysis. However, conventional partial differential equation based image denoising methods, especially total variation functional minimization techniques, do not work very well on biomedical images such as magnetic resonance images (MRI), ultrasound, and X-ray images. These images present small structures with signals barely detectable above the noise level which involve more complex noise and unclear edges. The recently developed non-local means (NLM) filtering method can treat these types of images better. The standard NLM filter uses the weighted averages of similar patches present in the image. Since the NLM filter is anon-local averaging method, it is very accurate in removing noise but has computational complexity. We develop efficient and optimized NLM based methods and their associate numerical algorithms. The new methods are still accurate enough and moreeffi-cient than the original NLM filter. Numerical results show that the new methods compare favorably to the conventional denoising methods.
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4

Tang, Song Yuan. "A Non-Local Image Denoising Technique Using Adaptive Filter Parameter." Applied Mechanics and Materials 556-562 (May 2014): 4839–42. http://dx.doi.org/10.4028/www.scientific.net/amm.556-562.4839.

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This paper proposes a method to obtain the optimal filter parameter of the non-local mean (NLM) algorithm. The parameter is assumed to be a function of the variance of the additive white Gaussian noise and is adaptive estimated. The initialization of the variance of the additive white Gaussian noise is estimated by Wiener filter. Then the NLM filter is used to adaptively estimate the noise variance. The image denoising is an iterative computation till the parameter convergence. Experiments show that the proposed method can improve the quality of the denoised images efficiently.
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5

Reddy, Kamireddy Rasool, Madhava Rao Ch, and Nagi Reddy Kalikiri. "Performance Assessment of Edge Preserving Filters." International Journal of Information System Modeling and Design 8, no. 2 (2017): 1–29. http://dx.doi.org/10.4018/ijismd.2017040101.

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Denoising is one of the important aspects in image processing applications. Denoising is the process of eliminating the noise from the noisy image. In most cases, noise accumulates at the edges. So that prevention of noise at edges is one of the most prominent problem. There are numerous edge preserving approaches available to reduce the noise at edges in that Gaussian filter, bilateral filter and non-local means filtering are the popular approaches but in these approaches denoised image suffer from blurring. To overcome these problems, in this article a Gaussian/bilateral filtering (G/BF) with a wavelet thresholding approach is proposed for better image denoising. The performance of the proposed work is compared with some edge-preserving filter algorithms such as a bilateral filter and the Non-Local Means Filter, in terms that objectively assess quality. From the simulation results, it is found that the performance of proposed method is superior to the bilateral filter and the Non-Local Means Filter.
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6

Wang, Gaihua, Yang Liu, Wei Xiong, and Yan Li. "An improved non-local means filter for color image denoising." Optik 173 (November 2018): 157–73. http://dx.doi.org/10.1016/j.ijleo.2018.08.013.

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7

Ben Said, Ahmed, Rachid Hadjidj, Kamal Eddine Melkemi, and Sebti Foufou. "Multispectral image denoising with optimized vector non-local mean filter." Digital Signal Processing 58 (November 2016): 115–26. http://dx.doi.org/10.1016/j.dsp.2016.07.017.

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8

Wu, Hongtao, Lei Jia, Ying Meng, Xiao Liu, and Jinhui Lan. "A Novel Adaptive Non-Local Means-Based Nonlinear Fitting for Visibility Improving." Symmetry 10, no. 12 (2018): 741. http://dx.doi.org/10.3390/sym10120741.

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The spatial-based method has become the most widely used method in improving the visibility of images. The visibility improving is mainly to remove the noise in the image, in order to trade off denoising and detail maintaining. A novel adaptive non-local means-based nonlinear fitting method is proposed in this paper. Firstly, according to the smoothness of the intensity around the central pixel, eight kinds of templates with different precision are exploited to approximate the central pixel through a novel adaptive non-local means filter design; the approximate weight coefficients of templates are derived from the approximation credibility. Subsequently, the fractal correction is used to smooth the denoising results. Eventually, the Rockafellar multiplier method is employed to generalize the smooth plane fitting to any geometric surface, thus yielding the optimal fitting of the center pixel approximation. Through a large number of experiments, it is clearly elucidated that compared with the classical spatial iteration-based methods and the recent denoising algorithms, the proposed algorithm is more robust and has better effect on denoising, while keeping more original details during denoising.
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9

LIU Qiao-hong, 刘巧红, 李斌 LI Bin, and 林敏 LIN Min. "Image denoising with dual-directional filter bank GSM model and non-local mean filter." Optics and Precision Engineering 22, no. 10 (2014): 2806–14. http://dx.doi.org/10.3788/ope.20142210.2806.

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10

Joshi, Nikita, Sarika Jain, and Amit Agarwal. "Discrete Total Variation-Based Non-Local Means Filter for Denoising Magnetic Resonance Images." Journal of Information Technology Research 13, no. 4 (2020): 14–31. http://dx.doi.org/10.4018/jitr.2020100102.

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Magnetic resonance (MR) images suffer from noise introduced by various sources. Due to this noise, diagnosis remains inaccurate. Thus, removal of noise becomes a very important task when dealing with MR images. In this paper, a denoising method has been discussed that makes use of non-local means filter and discrete total variation method. The proposed approach has been compared with other noise removal techniques like non-local means filter, anisotropic diffusion, total variation, and discrete total variation method, and it proves to be effective in reducing noise. The performance of various denoising methods is compared on basis of metrics such as peak signal-to-noise ratio (PSNR), mean square error (MSE), universal image quality index (UQI), and structure similarity index (SSIM) values. This method has been tested for various noise levels, and it outperformed other existing noise removal techniques, without blurring the image.
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