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

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

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1

NamAnh, Dao. "Image Denoising by Addaptive Non-Local Bilatetal Filter." International Journal of Computer Applications 99, no. 12 (August 20, 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 (July 24, 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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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 (April 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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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 (December 11, 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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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 (October 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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11

Chengzhi Deng, Wei Tian, Saifeng Hu, Yan Li, Min Hu, and Shengqian Wang. "Shearlet-based image denoising using adaptive thresholding and non-local means filter." International Journal of Digital Content Technology and its Applications 6, no. 20 (November 30, 2012): 333–42. http://dx.doi.org/10.4156/jdcta.vol6.issue20.36.

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12

Xu, Guangyu. "An Improved Non-local Filter for Image Denoising Using Steering Kernel Regression." Journal of Information and Computational Science 10, no. 15 (October 10, 2013): 4723–32. http://dx.doi.org/10.12733/jics20102179.

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13

Panigrahi, Susant Kumar, Supratim Gupta, and Prasanna K. Sahu. "Curvelet-based multiscale denoising using non-local means & guided image filter." IET Image Processing 12, no. 6 (June 1, 2018): 909–18. http://dx.doi.org/10.1049/iet-ipr.2017.0825.

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14

Wang, Jia, and ChangCheng Yin. "A Zernike-moment-based non-local denoising filter for cryo-EM images." Science China Life Sciences 56, no. 4 (April 2013): 384–90. http://dx.doi.org/10.1007/s11427-013-4467-3.

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15

Vijayaraghavan, V., and M. Karthikeyan. "Wavelet based Image Denoising with Locally Adaptive Window and Non-Local Means Filter." Asian Journal of Research in Social Sciences and Humanities 7, no. 3 (2017): 1080. http://dx.doi.org/10.5958/2249-7315.2017.00229.5.

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16

Coupé, P., J. V. Manjón, M. Robles, and D. L. Collins. "Adaptive multiresolution non-local means filter for three-dimensional magnetic resonance image denoising." IET Image Processing 6, no. 5 (2012): 558. http://dx.doi.org/10.1049/iet-ipr.2011.0161.

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17

Shreyamsha Kumar, B. K. "Image denoising based on non-local means filter and its method noise thresholding." Signal, Image and Video Processing 7, no. 6 (October 23, 2012): 1211–27. http://dx.doi.org/10.1007/s11760-012-0389-y.

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18

Zhang, Xiaobo. "Center pixel weight based on Wiener filter for non-local means image denoising." Optik 244 (October 2021): 167557. http://dx.doi.org/10.1016/j.ijleo.2021.167557.

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19

Park, Sohyeon, Geehyun Kim, and Gyemin Lee. "Image reconstruction for rotational modulation collimator (RMC) using non local means (NLM) denoising filter." Nuclear Instruments and Methods in Physics Research Section A: Accelerators, Spectrometers, Detectors and Associated Equipment 954 (February 2020): 161901. http://dx.doi.org/10.1016/j.nima.2019.02.028.

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20

Liu, Hong, Cihui Yang, Ning Pan, Enmin Song, and Richard Green. "Denoising 3D MR images by the enhanced non-local means filter for Rician noise." Magnetic Resonance Imaging 28, no. 10 (December 2010): 1485–96. http://dx.doi.org/10.1016/j.mri.2010.06.023.

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21

Wei, Dai-Yu, and Chang-Cheng Yin. "An optimized locally adaptive non-local means denoising filter for cryo-electron microscopy data." Journal of Structural Biology 172, no. 3 (December 2010): 211–18. http://dx.doi.org/10.1016/j.jsb.2010.06.021.

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22

Yang, Jian, Jingfan Fan, Danni Ai, Shoujun Zhou, Songyuan Tang, and Yongtian Wang. "Brain MR image denoising for Rician noise using pre-smooth non-local means filter." BioMedical Engineering OnLine 14, no. 1 (2015): 2. http://dx.doi.org/10.1186/1475-925x-14-2.

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23

Zhang, Xinyuan, Guirong Hou, Jianhua Ma, Wei Yang, Bingquan Lin, Yikai Xu, Wufan Chen, and Yanqiu Feng. "Denoising MR Images Using Non-Local Means Filter with Combined Patch and Pixel Similarity." PLoS ONE 9, no. 6 (June 16, 2014): e100240. http://dx.doi.org/10.1371/journal.pone.0100240.

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24

Mohan, J., V. Krishnaveni, and Yanhui Guo. "A New Neutrosophic Approach of Wiener Filtering for MRI Denoising." Measurement Science Review 13, no. 4 (August 1, 2013): 177–86. http://dx.doi.org/10.2478/msr-2013-0027.

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In this paper, a new filtering method based on neutrosophic set (NS) approach of wiener filter is presented to remove Rician noise from magnetic resonance image. A neutrosophic set, a part of neutrosophy theory, studies the origin, nature and scope of neutralities, as well as their interactions with different ideational spectra. Now, we apply the neutrosophic set into image domain and define some concepts and operators for image denoising. The image is transformed into NS domain, described using three membership sets: True (T), Indeterminacy (I) and False (F). The entropy of the neutrosophic set is defined and employed to measure the indeterminacy. The ω-wiener filtering operation is used on T and F to decrease the set indeterminacy and remove noise. The experiments have conducted on simulated Magnetic Resonance images (MRI) from Brainweb database and clinical MR images corrupted by Rician noise. The results show that the NS wiener filter produces better denoising results in terms of visual perception, qualitative and quantitative measures compared with other denoising methods, such as classical wiener filter, the anisotropic diffusion filter, the total variation minimization scheme and non local means filter.
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25

Li, N., R. Zhou, and X. Z. Zhao. "Mechanical faulty signal denoising using a redundant non-linear second-generation wavelet transform." Proceedings of the Institution of Mechanical Engineers, Part C: Journal of Mechanical Engineering Science 225, no. 4 (April 2011): 799–808. http://dx.doi.org/10.1243/09544062jmes2410.

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Denoising and extraction of the weak signals are crucial to mechanical equipment fault diagnostics, especially for early fault detection, in which cases fault features are very weak and masked by the noise. The wavelet transform has been widely used in mechanical faulty signal denoising due to its extraordinary timefrequency representation capability. However, the mechanical faulty signals are often non-stationary, with the structure varying significantly within each scale. Because a single wavelet filter cannot mimic the signal structure of an entire scale, the traditional wavelet-based signal denoising method cannot achieve an ideal effect, and even worse some faulty information of the raw signal may be lost in the denoising process. To overcome this deficiency, a novel mechanical faulty signal denoising method using a redundant non-linear second generation wavelet transform is proposed. In this method, an optimal prediction operator is selected for each transforming sample according to the selection criterion of minimizing each individual prediction error. Consequently, the selected predictor can always fit the local characteristics of the signals. The signal denoising results from both simulated signals and experimental data are presented and both support the proposed method.
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26

Huang, Hui, Shiyan Hu, and Ye Sun. "A Discrete Curvature Estimation Based Low-Distortion Adaptive Savitzky–Golay Filter for ECG Denoising." Sensors 19, no. 7 (April 4, 2019): 1617. http://dx.doi.org/10.3390/s19071617.

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Electrocardiogram (ECG) sensing is an important application for the diagnosis of cardiovascular diseases. Recently, driven by the emerging technology of wearable electronics, massive wearable ECG sensors are developed, which however brings additional sources of noise contamination on ECG signals from these wearable ECG sensors. In this paper, we propose a new low-distortion adaptive Savitzky-Golay (LDASG) filtering method for ECG denoising based on discrete curvature estimation, which demonstrates better performance than the state of the art of ECG denoising. The standard Savitzky-Golay (SG) filter has a remarkable performance of data smoothing. However, it lacks adaptability to signal variations and thus often induces signal distortion for high-variation signals such as ECG. In our method, the discrete curvature estimation is adapted to represent the signal variation for the purpose of mitigating signal distortion. By adaptively designing the proper SG filter according to the discrete curvature for each data sample, the proposed method still retains the intrinsic advantage of SG filters of excellent data smoothing and further tackles the challenge of denoising high signal variations with low signal distortion. In our experiment, we compared our method with the EMD-wavelet based method and the non-local means (NLM) denoising method in the performance of both noise elimination and signal distortion reduction. Particularly, for the signal distortion reduction, our method decreases in MSE by 33.33% when compared to EMD-wavelet and by 50% when compared to NLM, and decreases in PRD by 18.25% when compared to EMD-wavelet and by 25.24% when compared to NLM. Our method shows high potential and feasibility in wide applications of ECG denoising for both clinical use and consumer electronics.
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Yang, Lei, Richard Parton, Graeme Ball, Zhen Qiu, Alan H. Greenaway, Ilan Davis, and Weiping Lu. "An adaptive non-local means filter for denoising live-cell images and improving particle detection." Journal of Structural Biology 172, no. 3 (December 2010): 233–43. http://dx.doi.org/10.1016/j.jsb.2010.06.019.

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28

Jomaa, Hajer, Rostom Mabrouk, Nawres Khlifa, and Frédéric Morain-Nicolier. "Denoising of dynamic PET images using a multi-scale transform and non-local means filter." Biomedical Signal Processing and Control 41 (March 2018): 69–80. http://dx.doi.org/10.1016/j.bspc.2017.11.002.

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29

Kubicek, Jan, Michal Strycek, Martin Cerny, Marek Penhaker, Ondrej Prokop, and Dominik Vilimek. "Quantitative and Comparative Analysis of Effectivity and Robustness for Enhanced and Optimized Non-Local Mean Filter Combining Pixel and Patch Information on MR Images of Musculoskeletal System." Sensors 21, no. 12 (June 17, 2021): 4161. http://dx.doi.org/10.3390/s21124161.

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In the area of musculoskeletal MR images analysis, the image denoising plays an important role in enhancing the spatial image area for further processing. Recent studies have shown that non-local means (NLM) methods appear to be more effective and robust when compared with conventional local statistical filters, including median or average filters, when Rician noise is presented. A significant limitation of NLM is the fact that thy have the tendency to suppress tiny objects, which may represent clinically important information. For this reason, we provide an extensive quantitative and objective analysis of a novel NLM algorithm, taking advantage of pixel and patch similarity information with the optimization procedure for optimal filter parameters selection to demonstrate a higher robustness and effectivity, when comparing with NLM and conventional local means methods, including average and median filters. We provide extensive testing on variable noise generators with dynamical noise intensity to objectively demonstrate the robustness of the method in a noisy environment, which simulates relevant, variable and real conditions. This work also objectively evaluates the potential and benefits of the application of NLM filters in contrast to conventional local-mean filters. The final part of the analysis is focused on the segmentation performance when an NLM filter is applied. This analysis demonstrates a better performance of tissue identification with the application of smoothing procedure under worsening image conditions.
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Zhang, Xiao-hua, Jia-wei Chen, Hong-yun Meng, Li-cheng Jiao, and Xiang Sun. "A Non-local Means Filter Image Denoising with Directional Enhancement Neighborhood Windows and Non-subsampled Shearlet Feature Descriptors." Journal of Electronics & Information Technology 33, no. 11 (November 14, 2011): 2634–39. http://dx.doi.org/10.3724/sp.j.1146.2011.00221.

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31

Kagoiya, Kenneth, and Elijah Mwangi. "A Hybrid and Adaptive Non-Local Means Wavelet based MRI Denoising Method with Bilateral Filter Enhancement." International Journal of Computer Applications 166, no. 10 (May 17, 2017): 1–7. http://dx.doi.org/10.5120/ijca2017914121.

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32

Huhle, Benjamin, Timo Schairer, Philipp Jenke, and Wolfgang Straßer. "Fusion of range and color images for denoising and resolution enhancement with a non-local filter." Computer Vision and Image Understanding 114, no. 12 (December 2010): 1336–45. http://dx.doi.org/10.1016/j.cviu.2009.11.004.

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33

Chen, Xian Bo, Xing Hao Ding, and Hui Liu. "MRI Denoising Based on a Non-Parametric Bayesian Image Sparse Representation Method." Advanced Materials Research 219-220 (March 2011): 1354–58. http://dx.doi.org/10.4028/www.scientific.net/amr.219-220.1354.

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Magnetic Resonance images are often corrupted by Gaussian noise which highly affects the quality of MR images. In this paper, a Non-Parametric hierarchical Bayesian image sparse representation method is proposed to wipe out Gaussian distribution noise coupling in MR images. In this method a spike-slab prior is imposed on sparse coefficients, and a redundant dictionary is learned from the corrupted image. Experimental results show that the method not only improves the effect of MRI denoising, but also can obtain good estimation of the noise variance. Compared to non-local filter method, this model shows better visual quality as well as higher PSNR.
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Wu, Zhanxiong, Thomas Potter, Dongnan Wu, and Yingchun Zhang. "Denoising high angular resolution diffusion imaging data by combining singular value decomposition and non-local means filter." Journal of Neuroscience Methods 312 (January 2019): 105–13. http://dx.doi.org/10.1016/j.jneumeth.2018.11.020.

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35

Bhargava, Gollamandala, and Vaazi Sivakumar. "An Effective Method for Image Denoising Using Non-local Means and Statistics based Guided Filter in Nonsubsampled Contourlet Domain." International Journal of Intelligent Engineering and Systems 12, no. 3 (June 30, 2019): 76–87. http://dx.doi.org/10.22266/ijies2019.0630.09.

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36

Roscani, V., S. Tozza, M. Castellano, E. Merlin, D. Ottaviani, M. Falcone, and A. Fontana. "A comparative analysis of denoising algorithms for extragalactic imaging surveys." Astronomy & Astrophysics 643 (October 29, 2020): A43. http://dx.doi.org/10.1051/0004-6361/201936278.

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Aims. We present a comprehensive analysis of the performance of noise-reduction (denoising) algorithms to determine whether they provide advantages in source detection, mitigating noise on extragalactic survey images. Methods. The methods we analyze here are representative of different algorithmic families: Perona-Malik filtering, bilateral filter, total variation denoising, structure-texture image decomposition, non-local means, wavelets, and block-matching We tested the algorithms on simulated images of extragalactic fields with resolution and depth typical of the Hubble, Spitzer, and Euclid Space Telescopes, and of ground-based instruments. After choosing their best internal parameters configuration, we assessed their performance as a function of resolution, background level, and image type, in addition to testing their ability to preserve the objects fluxes and shapes. Finally, we analyze, in terms of completeness and purity, the catalogs that were extracted after applying denoising algorithms on a simulated Euclid Wide Survey VIS image and on real H160 and K-band (HAWK-I) observations of the CANDELS GOODS-South field. Results. Denoising algorithms often outperform the standard approach of filtering with the point spread function (PSF) of the image. Applying structure-texture image decomposition, Perona-Malik filtering, the total variation method by Chambolle, and bilateral filtering on the Euclid-VIS image, we obtain catalogs that are both more pure and complete by 0.2 magnitude than those based on the standard approach. The same result is achieved with the structure-texture image decomposition algorithm applied on the H160 image. The relative advantage of denoising techniques with respect to PSF filtering rises with increasing depth. Moreover, these techniques better preserve the shape of the detected objects with respect to PSF smoothing. Conclusions. Denoising algorithms provide significant improvements in the detection of faint objects and enhance the scientific return of current and future extragalactic surveys. We identify the most promising denoising algorithms among the 20 techniques considered in this study.
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Lopac, Nikola, Jonatan Lerga, and Elena Cuoco. "Gravitational-Wave Burst Signals Denoising Based on the Adaptive Modification of the Intersection of Confidence Intervals Rule." Sensors 20, no. 23 (December 3, 2020): 6920. http://dx.doi.org/10.3390/s20236920.

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Gravitational-wave data (discovered first in 2015 by the Advanced LIGO interferometers and awarded by the Nobel Prize in 2017) are characterized by non-Gaussian and non-stationary noise. The ever-increasing amount of acquired data requires the development of efficient denoising algorithms that will enable the detection of gravitational-wave events embedded in low signal-to-noise-ratio (SNR) environments. In this paper, an algorithm based on the local polynomial approximation (LPA) combined with the relative intersection of confidence intervals (RICI) rule for the filter support selection is proposed to denoise the gravitational-wave burst signals from core collapse supernovae. The LPA-RICI denoising method’s performance is tested on three different burst signals, numerically generated and injected into the real-life noise data collected by the Advanced LIGO detector. The analysis of the experimental results obtained by several case studies (conducted at different signal source distances corresponding to the different SNR values) indicates that the LPA-RICI method efficiently removes the noise and simultaneously preserves the morphology of the gravitational-wave burst signals. The technique offers reliable denoising performance even at the very low SNR values. Moreover, the analysis shows that the LPA-RICI method outperforms the approach combining LPA and the original intersection of confidence intervals (ICI) rule, total-variation (TV) based method, the method based on the neighboring thresholding in the short-time Fourier transform (STFT) domain, and three wavelet-based denoising techniques by increasing the improvement in the SNR by up to 118.94% and the peak SNR by up to 138.52%, as well as by reducing the root mean squared error by up to 64.59%, the mean absolute error by up to 55.60%, and the maximum absolute error by up to 84.79%.
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Bodavarapu, Pavan Nageswar Reddy, and P. V. V. S. Srinivas. "Facial expression recognition for low resolution images using convolutional neural networks and denoising techniques." Indian Journal of Science and Technology 14, no. 12 (March 27, 2021): 971–83. http://dx.doi.org/10.17485/ijst/v14i12.14.

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Background/Objectives: There is only limited research work is going on in the field of facial expression recognition on low resolution images. Mostly, all the images in the real world will be in low resolution and might also contain noise, so this study is to design a novel convolutional neural network model (FERConvNet), which can perform better on low resolution images. Methods: We proposed a model and then compared with state-of-art models on FER2013 dataset. There is no publicly available dataset, which contains low resolution images for facial expression recognition (Anger, Sad, Disgust, Happy, Surprise, Neutral, Fear), so we created a Low Resolution Facial Expression (LRFE) dataset, which contains more than 6000 images of seven types of facial expressions. The existing FER2013 dataset and LRFE dataset were used. These datasets were divided in the ratio 80:20 for training and testing and validation purpose. A HDM is proposed, which is a combination of Gaussian Filter, Bilateral Filter and Non local means denoising Filter. This hybrid denoising method helps us to increase the performance of the convolutional neural network. The proposed model was then compared with VGG16 and VGG19 models. Findings: The experimental results show that the proposed FERConvNet_HDM approach is effective than VGG16 and VGG19 in facial expression recognition on both FER2013 and LRFE dataset. The proposed FERConvNet_HDM approach achieved 85% accuracy on Fer2013 dataset, outperforming the VGG16 and VGG19 models, whose accuracies are 60% and 53% on Fer2013 dataset respectively. The same FERConvNet_HDM approach when applied on LRFE dataset achieved 95% accuracy. After analyzing the results, our FERConvNet_HDM approach performs better than VGG16 and VGG19 on both Fer2013 and LRFE dataset. Novelty/Applications: HDM with convolutional neural networks, helps in increasing the performance of convolutional neural networks in Facial expression recognition. Keywords: Facial expression recognition; facial emotion; convolutional neural network; deep learning; computer vision
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Shim, Jina, Myonggeun Yoon, and Youngjin Lee. "Quantitative study of fast non-local means-based denoising filter in chest X-ray imaging with lung nodule using three-dimensional printing." Optik 179 (February 2019): 1180–88. http://dx.doi.org/10.1016/j.ijleo.2018.10.118.

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40

Yin, Ming, Wei Liu, Xia Zhao, Qing-Wei Guo, and Rui-Feng Bai. "Image denoising using trivariate prior model in nonsubsampled dual-tree complex contourlet transform domain and non-local means filter in spatial domain." Optik 124, no. 24 (December 2013): 6896–904. http://dx.doi.org/10.1016/j.ijleo.2013.05.132.

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Latha, S., and Dhanalakshmi Samiappan. "Despeckling of Carotid Artery Ultrasound Images with a Calculus Approach." Current Medical Imaging Formerly Current Medical Imaging Reviews 15, no. 4 (April 11, 2019): 414–26. http://dx.doi.org/10.2174/1573405614666180402124438.

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<P>Background: Carotid artery images indicate any presence of plaque content, which may lead to atherosclerosis and stroke. Early identification of the disease is possible by taking B-mode ultrasound images in the carotid artery. Speckle is the inherent noise content in the ultrasound images, which essentially needs to be minimized. </P><P> Objective: The objective of the proposed method is to convert the multiplicative speckle noise into additive, after which the frequency transformations can be applied. </P><P> Method: The method uses simple differentiation and integral calculus and is named variable gradient summation. It differs from the conventional homomorphic filter, by preserving the edge features to a great extent and better denoising. The additive image is subjected to wavelet decomposition and further speckle filtering with three different filters Non Local Means (NLM), Vectorial Total Variation (VTV) and Block Matching and 3D filtering (BM3D) algorithms. By this approach, the components dependent on the image are identified and the unwanted noise content existing in the high frequency portion of the image is removed. </P><P> Results & Conclusion: Experiments conducted on a set of 300 B-mode ultrasound carotid artery images and the simulation results prove that the proposed method of denoising gives enhanced results as compared to the conventional process in terms of the performance evaluation methods like peak signal to noise ratio, mean square error, mean absolute error, root mean square error, structural similarity, quality factor, correlation and image enhancement factor.</P>
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42

Bi, Wenda, Yonghui Zhao, Cong An, and Shufan Hu. "Clutter Elimination and Random-Noise Denoising of GPR Signals Using an SVD Method Based on the Hankel Matrix in the Local Frequency Domain." Sensors 18, no. 10 (October 12, 2018): 3422. http://dx.doi.org/10.3390/s18103422.

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Ground-penetrating radar (GPR) is a kind of high-frequency electromagnetic detection technology. It is mainly used to locate targets and interfaces in underground structures. In addition to the effective signals reflected from the subsurface objects or interfaces, the GPR signals in field work also include noise and different clutters, such as antenna-coupled waves, ground clutters, and radio-frequency interference, which have similar wavelet spectral characteristics with the target signals. Clutter and noise seriously interfere with the target’s response signal. The singular value decomposition (SVD) filtering method can select appropriate singular values and characteristic components corresponding to the effective signals for signal reconstruction to filter the GPR data. However, the conventional time-domain SVD method introduces fake signals when eliminating direct waves, and does not have good suppression of random noise around non-horizontal phase axes. Here, an SVD method based on the Hankel matrix in the local frequency domain of GPR data is proposed. Different numerical models and real field GPR data were handled using the proposed method. Based on the power of fake signals introduced via different processes, qualitative and quantitative analyses were carried out. The comparison shows that the newly proposed method could improve efforts to suppress random noise around non-horizontal phase reflection events and weaken the horizontal fake signals introduced by eliminating clutter such as ground waves.
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43

Szczepański, Marek, and Filip Giemza. "Noise Removal in the Developing Process of Digital Negatives." Sensors 20, no. 3 (February 7, 2020): 902. http://dx.doi.org/10.3390/s20030902.

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Most modern color digital cameras are equipped with a single image sensor with a color filter array (CFA). One of the most important stages of preprocessing is noise reduction. Most research related to this topic ignores the problem associated with the actual color image acquisition process and assumes that we are processing the image in the sRGB space. In the presented paper, the real process of developing raw images obtained from the CFA sensor was analyzed. As part of the work, a diverse database of test images in the form of a digital negative and its reference version was prepared. The main problem posed in the work was the location of the denoising and demosaicing algorithms in the entire raw image processing pipeline. For this purpose, all stages of processing the digital negative are reproduced. The process of noise generation in the image sensors was also simulated, parameterizing it with ISO sensitivity for a specific CMOS sensor. In this work, we tested commonly used algorithms based on the idea of non-local means, such as NLM or BM3D, in combination with various techniques of interpolation of CFA sensor data. Our experiments have shown that the use of noise reduction methods directly on the raw sensor data, improves the final result only in the case of highly disturbed images, which corresponds to the process of image acquisition in difficult lighting conditions.
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44

Kim, Bae-Guen, Seong-Hyeon Kang, Chan Rok Park, Hyun-Woo Jeong, and Youngjin Lee. "Noise Level and Similarity Analysis for Computed Tomographic Thoracic Image with Fast Non-Local Means Denoising Algorithm." Applied Sciences 10, no. 21 (October 23, 2020): 7455. http://dx.doi.org/10.3390/app10217455.

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Although conventional denoising filters have been developed for noise reduction from digital images, these filters simultaneously cause blurring in the images. To address this problem, we proposed the fast non-local means (FNLM) denoising algorithm which would preserve the edge information of objects better than conventional denoising filters. In this study, we obtained thoracic computed tomography (CT) images from a male adult mesh (MASH) phantom modeled by computer and a five-year-old phantom to perform both the simulation study and the practical study. Subsequently, the FNLM denoising algorithm and conventional denoising filters, such as the Gaussian, median, and Wiener filters, were applied to the MASH phantom image adding Gaussian noise with a standard deviation of 0.002 and practical CT images. Finally, the results were compared quantitatively in terms of the coefficient of variation (COV), contrast-to-noise ratio (CNR), peak signal-to-noise ratio (PSNR), and correlation coefficient (CC). The results showed that the FNLM denoising algorithm was more efficient than the conventional denoising filters. In conclusion, through the simulation study and the practical study, this study demonstrated the feasibility of the FNLM denoising algorithm for noise reduction from thoracic CT images.
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45

Zhang, Xiang-Song, Wei-Xin Gao, and Shi-Ling Zhu. "Research on Noise Reduction and Enhancement Algorithm of Girth Weld Image." Signal & Image Processing : An International Journal 12, no. 1 (February 28, 2021): 9–21. http://dx.doi.org/10.5121/sipij.2021.12102.

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In order to eliminate the salt pepper and Gaussian mixed noise in X-ray weld image, the extreme value characteristics of salt and pepper noise are used to separate the mixed noise, and the non local mean filtering algorithm is used to denoise it. Because the smoothness of the exponential weighted kernel function is too large, it is easy to cause the image details fuzzy, so the cosine coefficient based on the function is adopted. An improved non local mean image denoising algorithm is designed by using weighted Gaussian kernel function. The experimental results show that the new algorithm reduces the noise and retains the details of the original image, and the peak signal-to-noise ratio is increased by 1.5 dB. An adaptive salt and pepper noise elimination algorithm is proposed, which can automatically adjust the filtering window to identify the noise probability. Firstly, the median filter is applied to the image, and the filtering results are compared with the pre filtering results to get the noise points. Then the weighted average of the middle three groups of data under each filtering window is used to estimate the image noise probability. Before filtering, the obvious noise points are removed by threshold method, and then the central pixel is estimated by the reciprocal square of the distance from the center pixel of the window. Finally, according to Takagi Sugeno (T-S) fuzzy rules, the output estimates of different models are fused by using noise probability. Experimental results show that the algorithm has the ability of automatic noise estimation and adaptive window adjustment. After filtering, the standard mean square deviation can be reduced by more than 20%, and the speed can be increased more than twice. In the enhancement part, a nonlinear image enhancement method is proposed, which can adjust the parameters adaptively and enhance the weld area automatically instead of the background area. The enhancement effect achieves the best personal visual effect. Compared with the traditional method, the enhancement effect is better and more in line with the needs of industrial field.
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46

Wahid, Farha Fatina, K. Sugandhi, and G. Raju. "Cluster-based non-local filters for colour image denoising." International Journal of Digital Signals and Smart Systems 2, no. 3 (2018): 185. http://dx.doi.org/10.1504/ijdsss.2018.097306.

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47

G, RAJU, SUGANDHI K, and FARHA FATINA WAHID. "Cluster based Non-Local Filters for Colour Image denoising." International Journal of Digital Signals and Smart Systems 2, no. 3 (2018): 1. http://dx.doi.org/10.1504/ijdsss.2018.10017969.

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48

Mevenkamp, Niklas, Benjamin Berkels, and Martial Duchamp. "Denoising Electron-energy Loss Data Using Non-local Means Filters." Microscopy and Microanalysis 23, S1 (July 2017): 106–7. http://dx.doi.org/10.1017/s1431927617001210.

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49

MR. SANJAY SHITOLE, MRS RUPALI KALE,. "Analysis of Crop disease detection with SVM, KNN and Random forest classification." INFORMATION TECHNOLOGY IN INDUSTRY 9, no. 1 (March 1, 2021): 364–72. http://dx.doi.org/10.17762/itii.v9i1.140.

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Due to an uneven climatic condition crops are being affected which leads to decrease in agriculture yield. It greatly affects global agricultural economy. However, the condition becomes more worse when diseases are identified in crops. Agriculture plays a vital role in every country’s economy. Thus, there is a need to identify the crop disease before it is visible on a crop so that disease can be avoided by using appropriate measures. The traditional way of identifying a crop disease is through observation by naked eyes. But as it requires large number of experts and continuous monitoring of crop it will be costly for large fields. Hence, an automatic system is required which can not only examine the crops to detect disease but also can classify the type of disease on crops. The proposed system determines disease from an input image. The input image has to go through following stages: Image Acquisition, Image pre-processing, Image segmentation, Feature Extraction, and Classification in order to determine diseased crop and accordingly provides remedy for that disease. Infected crop image is taken as input in Image Acquisition stage. In Image pre-processing stage noise is removed from the input image by applying gaussian blur filter and non-local means denoising algorithm. Also, the background of image is eliminated using Thresholding algorithm. To extract Region of Interest (ROI) i.e. infected region from input image K-means Clustering algorithm is used. Then color, texture and shape features are extracted from ROI and features are further send to the classification stage. Three different classification algorithms namely Support Vector Machine (SVM), K-Nearest Neighbors (KNN) and Random Forest are implemented for classification out of which Support Vector Machine Algorithm is found to be best in terms of accuracy. Hence, classification is carried out by using Multivariate Support Vector Machine algorithm which detect disease present in crop accurately. In this way, the proposed system detects a disease from the given input image.
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50

Al-antari, Mugahed A., Mohammed A. Al-masni, Mohamed K. Metwally, Dildar Hussain, Se-Je Park, Jeong-Sik Shin, Seung-Moo Han, and Tae-Seong Kim. "Denoising images of dual energy X-ray absorptiometry using non-local means filters." Journal of X-Ray Science and Technology 26, no. 3 (May 25, 2018): 395–412. http://dx.doi.org/10.3233/xst-17341.

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