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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"

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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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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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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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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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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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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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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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Dissertations / Theses on the topic "Non-local denoising filter"

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Almahdi, Redha A. "Recursive Non-Local Means Filter for Video Denoising." University of Dayton / OhioLINK, 2016. http://rave.ohiolink.edu/etdc/view?acc_num=dayton1481033972368771.

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Karam, Christina Maria. "Acceleration of Non-Linear Image Filters, and Multi-Frame Image Denoising." University of Dayton / OhioLINK, 2019. http://rave.ohiolink.edu/etdc/view?acc_num=dayton1575976497271633.

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Guillemot, Thierry. "Méthodes et structures non locales pour la restaurationd'images et de surfaces 3D." Thesis, Paris, ENST, 2014. http://www.theses.fr/2014ENST0006/document.

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Durant ces dernières années, les technologies d’acquisition numériques n’ont cessé de se perfectionner, permettant d’obtenir des données d’une qualité toujours plus fine. Néanmoins, le signal acquis reste corrompu par des défauts qui ne peuvent être corrigés matériellement et nécessitent l’utilisation de méthodes de restauration adaptées. J'usqu’au milieu des années 2000, ces approches s’appuyaient uniquement sur un traitement local du signal détérioré. Avec l’amélioration des performances de calcul, le support du filtre a pu être étendu à l’ensemble des données acquises en exploitant leur caractère autosimilaire. Ces approches non locales ont principalement été utilisées pour restaurer des données régulières et structurées telles que des images. Mais dans le cas extrême de données irrégulières et non structurées comme les nuages de points 3D, leur adaptation est peu étudiée à l’heure actuelle. Avec l’augmentation de la quantité de données échangées sur les réseaux de communication, de nouvelles méthodes non locales ont récemment été proposées. Elles utilisent un modèle a priori extrait à partir de grands ensembles d’échantillons pour améliorer la qualité de la restauration. Néanmoins, ce type de méthode reste actuellement trop coûteux en temps et en mémoire. Dans cette thèse, nous proposons, tout d’abord, d’étendre les méthodes non locales aux nuages de points 3D, en définissant une surface de points capable d’exploiter leur caractère autosimilaire. Nous introduisons ensuite une nouvelle structure de données, le CovTree, flexible et générique, capable d’apprendre les distributions d’un grand ensemble d’échantillons avec une capacité de mémoire limitée. Finalement, nous généralisons les méthodes de restauration collaboratives appliquées aux données 2D et 3D, en utilisant notre CovTree pour apprendre un modèle statistique a priori à partir d’un grand ensemble de données
In recent years, digital technologies allowing to acquire real world objects or scenes have been significantly improved in order to obtain high quality datasets. However, the acquired signal is corrupted by defects which can not be rectified materially and require the use of adapted restoration methods. Until the middle 2000s, these approaches were only based on a local process applyed on the damaged signal. With the improvement of computing performance, the neighborhood used by the filter has been extended to the entire acquired dataset by exploiting their self-similar nature. These non-local approaches have mainly been used to restore regular and structured data such as images. But in the extreme case of irregular and unstructured data as 3D point sets, their adaptation is few investigated at this time. With the increase amount of exchanged data over the communication networks, new non-local methods have recently been proposed. These can improve the quality of the restoration by using an a priori model extracted from large data sets. However, this kind of method is time and memory consuming. In this thesis, we first propose to extend the non-local methods for 3D point sets by defining a surface of points which exploits their self-similar of the point cloud. We then introduce a new flexible and generic data structure, called the CovTree, allowing to learn the distribution of a large set of samples with a limited memory capacity. Finally, we generalize collaborative restoration methods applied to 2D and 3D data by using our CovTree to learn a statistical a priori model from a large dataset

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Juráček, Ivo. "Zabezpečení senzorů - ověření pravosti obrazu." Master's thesis, Vysoké učení technické v Brně. Fakulta informačních technologií, 2020. http://www.nusl.cz/ntk/nusl-432921.

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Diploma thesis is about image sensor security. Goal of the thesis was study data integrity gained from the image sensors. Proposed method is about source camera identification from noise characteristics in image sensors. Research was about influence of denoising algorithms applied to digital images, which was acquired from 15 different image sensors. Finally the statistical evaluation had been done from computed results.

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Válek, Matěj. "Aproximativní implementace aritmetických operací v obrazových filtrech." Master's thesis, Vysoké učení technické v Brně. Fakulta informačních technologií, 2021. http://www.nusl.cz/ntk/nusl-445540.

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Tato diplomová práce se zabývá aproximativní implementace aritmetických operací v obrazových filtrech. Zejména tedy využitím aproximativních technik pro úpravu způsobu násobení v netriviálním obrazovém filtru. K tomu je využito několik technik, jako použití převodu násobení s pohyblivou řadovou čárkou na násobení s pevnou řadovou čárkou, či využití evolučních algoritmů zejména kartézkého genetického programování pro vytvoření nových aproximovaných násobiček, které vykazují přijatelnou chybu, ale současně redukují výpočetní náročnost filtrace. Výsledkem jsou evolučně navržené aproximativní násobičky zohledňující distribuci dat v obrazovém filtru a jejich nasazení v obrazovém filtru a porovnání původního filtru s aproximovaným fitrem na sadě barevných obrázků.

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Book chapters on the topic "Non-local denoising filter"

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Aparna, P. L., Rahul G. Waghmare, Deepak Mishra, and R. K. Sai Subrahmanyam Gorthi. "Effective Denoising with Non-local Means Filter for Reliable Unwrapping of Digital Holographic Interferometric Fringes." In Proceedings of 2nd International Conference on Computer Vision & Image Processing, 13–24. Singapore: Springer Singapore, 2018. http://dx.doi.org/10.1007/978-981-10-7895-8_2.

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Kumar Sharma, Krishna, Dheeraj Gurjar, Monika Jyotyana, and Vinod Kumari. "Denoising of Brain MRI Images Using a Hybrid Filter Method of Sylvester-Lyapunov Equation and Non Local Means." In Smart Innovations in Communication and Computational Sciences, 495–505. Singapore: Springer Singapore, 2018. http://dx.doi.org/10.1007/978-981-13-2414-7_46.

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Rabbouch, Hana, Othman Ben Messaoud, and Foued Saâdaoui. "Multi-scaled Non-local Means Parallel Filters for Medical Image Denoising." In Algorithms and Architectures for Parallel Processing, 606–13. Cham: Springer International Publishing, 2020. http://dx.doi.org/10.1007/978-3-030-60245-1_41.

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Rajagopal, Sivakumar, and Babu Gopal. "Effective and Accurate Diagnosis Using Brain Image Fusion." In Applications of Deep Learning and Big IoT on Personalized Healthcare Services, 197–217. IGI Global, 2020. http://dx.doi.org/10.4018/978-1-7998-2101-4.ch012.

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Medical imaging techniques are routinely employed to create images of the human system for clinical purposes. Multi-modality medical imaging is a widely used technology for diagnosis, detection, and prediction of various tissue abnormalities. This chapter is focused on the development of an improved brain image processing technique for the removal of noise from a magnetic resonance image (MRI) for accurate image restoration. Feature selection and extraction of MRI brain images are processed using image fusion. The medical images suffer from motion blur and noise for which image denoising is developed through non-local means (NLM) filtering for smoothing and shrinkage rule for sharpening. The peak signal to noise ratio (PSNR) of improved curvelet based self-similarity NLM method is better than discrete wavelet transform with an NLM filter.

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Conference papers on the topic "Non-local denoising filter"

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Wen-Qiang Feng, Shu-Min Li, and Ke-Long Zheng. "A non-local bilateral filter for image denoising." In 2010 International Conference on Apperceiving Computing and Intelligence Analysis (ICACIA). IEEE, 2010. http://dx.doi.org/10.1109/icacia.2010.5709895.

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Li, Ming. "An Improved Non-Local Filter for Image Denoising." In 2009 International Conference on Information Engineering and Computer Science. IEEE, 2009. http://dx.doi.org/10.1109/iciecs.2009.5363902.

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Al-antari, M. A., M. A. Al-masni, M. Metwally, D. Hussain, E. Valarezo, P. Rivera, G. Gi, et al. "Non-local means filter denoising for DEXA images." In 2017 39th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC). IEEE, 2017. http://dx.doi.org/10.1109/embc.2017.8036889.

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Joshi, Nikita, Sarika Jain, and Amit Agarwal. "Segmentation based non local means filter for denoising MRI." In 2017 6th International Conference on Reliability, Infocom Technologies and Optimization (Trends and Future Directions) (ICRITO). IEEE, 2017. http://dx.doi.org/10.1109/icrito.2017.8342506.

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Zhan, Yi, Mingyue Ding, Feng Xiao, and Xuming Zhang. "An Improved Non-local Means Filter for Image Denoising." In 2011 International Conference on Intelligent Computation and Bio-Medical Instrumentation (ICBMI). IEEE, 2011. http://dx.doi.org/10.1109/icbmi.2011.5.

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Chen, Runpu, Weidong Yu, Yunkai Deng, Robert Wang, Gang Liu, and Yunfeng Shao. "Pyramid non-local mean filter for interferometric phase denoising." In IGARSS 2012 - 2012 IEEE International Geoscience and Remote Sensing Symposium. IEEE, 2012. http://dx.doi.org/10.1109/igarss.2012.6350528.

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Joshi, Nikita, Sarika Jain, and Amit Agarwal. "An improved approach for denoising MRI using non local means filter." In 2016 2nd International Conference on Next Generation Computing Technologies (NGCT). IEEE, 2016. http://dx.doi.org/10.1109/ngct.2016.7877492.

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Baqar, Mohtashim, Sian Lun Lau, and Mansoor Ebrahim. "GMSD-based Perceptually Motivated Non-local Means Filter for Image Denoising." In 2019 IEEE International Symposium on Haptic, Audio and Visual Environments and Games (HAVE). IEEE, 2019. http://dx.doi.org/10.1109/have.2019.8921188.

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Almahdi, Redha, and Russell C. Hardie. "Recursive non-local means filter for video denoising with Poisson-Gaussian noise." In 2016 IEEE National Aerospace and Electronics Conference (NAECON) and Ohio Innovation Summit (OIS). IEEE, 2016. http://dx.doi.org/10.1109/naecon.2016.7856822.

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Fu, Bo, Ruizi Wang, Yi Li, and Chengdi Xing. "Non-Local Directional-Guided Filter for Impulse-Gaussian Mixed Noise Image Denoising." In 2019 IEEE 14th International Conference on Intelligent Systems and Knowledge Engineering (ISKE). IEEE, 2019. http://dx.doi.org/10.1109/iske47853.2019.9170405.

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