Relevant bibliographies by topics / Denoising Filters

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  1. Journal articles
  2. Dissertations / Theses
  3. Book chapters
  4. Conference papers

Academic literature on the topic 'Denoising Filters'

Author: Grafiati
Published: 3 June 2025
Last updated: 31 July 2025

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Journal articles on the topic "Denoising Filters"

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Dai, Ming Hong. "The Development of Wavelet Transform and its Application in Image Denoise." Advanced Materials Research 694-697 (May 2013): 2003–8. http://dx.doi.org/10.4028/www.scientific.net/amr.694-697.2003.

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The paper introduces Laplace pyramid, Ridgelet and Curvelet principle, structure and methods, and their denoising experimental studies. It also introduces the traditional direction filter of principle, structure and methodology, and the simulation experiments show that its image denoising PSNR is slightly lower than wavelet but denoising image visual quality is better than former. To that end, proposed a new direction filters that uniform direction filter banks and non-uniform direction filters, proved filter passband condition and related design and implementation issues were discussed. nonli
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A., Afreen Habiba, and Dr.B.Raghu2. "IMAGE DENOISING IN MRI IMAGES USING CONTOURLET TRANSFORM AND COMPARISON OF FILTERING METHODS." GLOBAL JOURNAL OF ENGINEERING SCIENCE AND RESEARCHES 4, no. 7 (2017): 150–60. https://doi.org/10.5281/zenodo.835762.

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In diagnosis of medical images, operations such as feature extraction and object recognition plays the key role.. These operations will become difficult if the images are corrupted with noises. Several types of noise were introduced in the images during image acquisition, transfer & storage. The main objective is to remove the noise from the input image. Image Denoising is an utmost challenge for Researchers, developing Image denoising algorithms is a difficult task, since fine details in a medical image should not be destroyed during noise removal during the diagnosis of information. Medi
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Salehi, Hadi, and Javad Vahidi. "An Ultrasound Image Despeckling Method Based on Weighted Adaptive Bilateral Filter." International Journal of Image and Graphics 20, no. 03 (2020): 2050020. http://dx.doi.org/10.1142/s0219467820500205.

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Images are widely used in engineering. Unfortunately, ultrasound images are mainly degraded by an intrinsic noise called speckle. Therefore, de-speckling is a critical preprocessing step. Therefore, a robust despeckling method and accurate evaluation of images are suggested. We suggest three phases and a three-step denoising filter. In the first phase, the coefficients of variation are computed from the noisy image. The second phase is a three-step denoising filter. The first step is denoising of extreme levels of homogeneous regions, based on fuzzy homogeneous regions. The second step is a pr
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Gallegos-Funes, F., J. Martínez-Valdés, R. Cruz-Santiago, and J. López-Bonilla. "Wavelet Order Statistics Filters for Image Denoising." Journal of Scientific Research 1, no. 2 (2009): 248–57. http://dx.doi.org/10.3329/jsr.v1i2.2311.

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This paper presents the wavelet order statistics filters for the removal of impulsive and speckle noise in color image applications. The proposed filtering scheme is defined as two filters in the wavelet domain to conform to the structure of a general filter that can be modified in some headings. The first filter is based on redundancy of approaches and the second one is the wavelet domain iterative center weighted median algorithm. With the structure of the proposed filter different implementations for the estimation of the noisy sample are carried out using different order statistics algorit
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Biradar, Nagashettappa, M. L. Dewal, and Manoj Kumar Rohit. "Edge Preserved Speckle Noise Reduction Using Integrated Fuzzy Filters." International Scholarly Research Notices 2014 (October 30, 2014): 1–11. http://dx.doi.org/10.1155/2014/876434.

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Echocardiographic images are inherent with speckle noise which makes visual reading and analysis quite difficult. The multiplicative speckle noise masks finer details, necessary for diagnosis of abnormalities. A novel speckle reduction technique based on integration of geometric, wiener, and fuzzy filters is proposed and analyzed in this paper. The denoising applications of fuzzy filters are studied and analyzed along with 26 denoising techniques. It is observed that geometric filter retains noise and, to address this issue, wiener filter is embedded into the geometric filter during iteration
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Jiang, Hai Bo, and Jing Zhi Cai. "Performance Analysis of Several Common Filter." Applied Mechanics and Materials 220-223 (November 2012): 1446–49. http://dx.doi.org/10.4028/www.scientific.net/amm.220-223.1446.

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Denoising is the initial stage of image processing, in preparation for the subsequent processing of the image. This article describes a field of several denoising used filters include average filter, median filter, Wiener filter, Kalman filter. Combination of diagrams, will describe their filtering principle, at the end of this paper,analysis signal to noise ratio of image and other performance indicators .
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Snehalatha, M., Dr N. Ramamurthy, K. Swetha, K. Vishnupriya, P. Sreelekha, and N. Niharika. "An Image Denoising in Spatial Domain using Bilateral Filter." Journal of Electronics and Communication Systems 7, no. 2 (2022): 9–14. http://dx.doi.org/10.46610/joecs.2022.v07i02.002.

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The use of bilateral filters is one of the most efficient and resource-saving image processing techniques. This method does not use edge smoothing to filter the image, but it does use non-linear spatial averaging. The characteristics of the filters in the filtering process outlined above are quite important. The outputs and results are dramatically affected by even minor changes in filter parameter values. The author contributed two pieces to this publication. The author has contributed to the study of parameter selection of bilateral filters that are optimal in nature in the context of pictur
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Bui, Ngoc Ha, Tien Hung Bui, Thuy Duong Tran, Kim Tuan Tran, and Ngoc Toan Tran. "Evaluation of the effect of filters on reconstructed image quality from cone beam CT system." Nuclear Science and Technology 11, no. 1 (2021): 35–47. http://dx.doi.org/10.53747/jnst.v11i1.130.

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: 3D Filtered Back Projection (FBP) is a three-dimensional reconstruction algorithm usually used in Cone Beam Computed Tomography (CBCT) system. FBP is one of the most popular algorithms due to its reconstruction is fast while quality of the result is acceptable. It can also handle a more considerable amount of data with same computer performance with other algorithms. However, the quality of a reconstructed image by the FBP algorithm strongly depends on spatial filters and denoising filters applied to projections. In this paper an evaluation of the reconstructed image quality of the CBCT syst
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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 (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 algor
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Muhammad Fathi Mohd Zain, Wan Mahani Hafizah Wan Mahmud, and Hong-Seng Gan. "Preliminary Analysis on the Effect of Different Denoising Techniques towards Texture Features of MRI Images of Alzheimer’s Disease." Journal of Advanced Research in Applied Sciences and Engineering Technology 31, no. 2 (2023): 234–44. http://dx.doi.org/10.37934/araset.31.2.234244.

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Early detection of Alzheimer’s disease (AD) has become one of the major research topics nowadays. The utilization of the computerized system may help medical experts to better understand and analyse the magnetic resonance imaging (MRI) images of AD patients for early detection. One of the commonly steps taken for the analysis of the image is image denoising using certain filters. However, finding shows that previous researchers use different approaches. This study aims to analyse the effect of different denoising techniques towards detection of Alzheimer’s disease. Data of two different groups
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Dissertations / Theses on the topic "Denoising Filters"

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MOTTA, THIAGO RIBEIRO DA. "AN EVALUATION OF 2D FILTERS FOR SPECKLE DENOISING ULTRASOUND EXAMS." PONTIFÍCIA UNIVERSIDADE CATÓLICA DO RIO DE JANEIRO, 2017. http://www.maxwell.vrac.puc-rio.br/Busca_etds.php?strSecao=resultado&nrSeq=33354@1.

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PONTIFÍCIA UNIVERSIDADE CATÓLICA DO RIO DE JANEIRO<br>COORDENAÇÃO DE APERFEIÇOAMENTO DO PESSOAL DE ENSINO SUPERIOR<br>CONSELHO NACIONAL DE DESENVOLVIMENTO CIENTÍFICO E TECNOLÓGICO<br>PROGRAMA DE EXCELENCIA ACADEMICA<br>Exames de ultrassom são uma ferramenta popular de aquisição de imagens na medicina atual por ser um procedimento não-invasivo, seguro e barato. Entretanto, inerente a qualquer exame de ultrassom encontra-se o ruído speckle, responsável pela degradação da imagem e dificultando tanto sua interpretação por parte de médicos e pacientes, quanto prejudicando a acurácia de métodos comp
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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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Chan, Tai Wai. "Applications of spatial varying filter on image interpolation, demosaicing and video denosing [i.e. denoising] /." View abstract or full-text, 2006. http://library.ust.hk/cgi/db/thesis.pl?ECED%202006%20CHANT.

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DeNooyer, Eric-Jan D. "Statistical Idealities and Expected Realities in the Wavelet Techniques Used for Denoising." Scholar Commons, 2010. http://scholarcommons.usf.edu/etd/3929.

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In the field of signal processing, one of the underlying enemies in obtaining a good quality signal is noise. The most common examples of signals that can be corrupted by noise are images and audio signals. Since the early 1980's, a time when wavelet transformations became a modernly defined tool, statistical techniques have been incorporated into processes that use wavelets with the goal of maximizing signal-to-noise ratios. We provide a brief history of wavelet theory, going back to Alfréd Haar's 1909 dissertation on orthogonal functions, as well as its important relationship to the earlier
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Duramaz, Alper. "Image Segmentation Based On Variational Techniques." Master's thesis, METU, 2006. http://etd.lib.metu.edu.tr/upload/12607721/index.pdf.

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Recently, solutions to the problem of image segmentation and denoising are developed based on the Mumford-Shah model. The model provides an energy functional, called the Mumford-Shah functional, which should be minimized. Since the minimization of the functional has some difficulties, approximate approaches are proposed. Two such methods are the gradient flows method and the Chan-Vese active contour method. The performance evolution in terms of speed shows that the gradient flows method converges to the boundaries of the smooth parts faster<br>but for the hierarchical four-phase segmentation,
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De, Santis Simone. "Quantum Median Filter for Total Variation denoising." Master's thesis, Alma Mater Studiorum - Università di Bologna, 2022.

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In this work we present Quantum Median Filter, an image processing algorithm for applying Total Variation denoising to quantum image representations. After a brief introduction to TV model and quantum computing, we present QMF algorithm and discuss its design and efficiency; then we implement and simulate the quantum circuit using Qiskit library; finally we apply it to a set of noisy images, in order to compare and evaluate experimental results.
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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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Kassakian, Peter William. "Audio denoising using wavelet filter banks aimed at real-time application." Thesis, Massachusetts Institute of Technology, 1999. http://hdl.handle.net/1721.1/80508.

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Raichl, Jaroslav. "Zpracování obrazů raných smrkových kultur snímaných MR technikou." Master's thesis, Vysoké učení technické v Brně. Fakulta elektrotechniky a komunikačních technologií, 2009. http://www.nusl.cz/ntk/nusl-217985.

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This semester project deals with filtering of the images detected by use of NMR obtained by NMR application measurement of nuclear magnetic resonance (NMR). This thesis includes the theory of nuclear magnetic resonance, digital filters, basic digital filter banks structures, theory of Wavelet transformation and description of Signal to Noise Ratio calculation. Basic procedure of the MR signal denoising is summarized in the theoretical part of the thesis. The denoising of the images detected by nuclear magnetic resonance is described. In experimental part filtering methods for images denoising
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Deng, Hao. "Mathematical approaches to digital color image denoising." Diss., Atlanta, Ga. : Georgia Institute of Technology, 2009. http://hdl.handle.net/1853/31708.

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Thesis (Ph.D)--Mathematics, Georgia Institute of Technology, 2010.<br>Committee Chair: Haomin Zhou; Committee Member: Luca Dieci; Committee Member: Ronghua Pan; Committee Member: Sung Ha Kang; Committee Member: Yang Wang. Part of the SMARTech Electronic Thesis and Dissertation Collection.
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Book chapters on the topic "Denoising Filters"

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Rosales-Silva, Alberto, Volodymyr I. Ponomaryov, and Francisco J. Gallegos-Funes. "Fuzzy Vector Directional Filters for Multichannel Image Denoising." In Lecture Notes in Computer Science. Springer Berlin Heidelberg, 2007. http://dx.doi.org/10.1007/978-3-540-76725-1_14.

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Oza, Shraddha D., and K. R. Joshi. "Performance Analysis of Denoising Filters for MR Images." In Advances in Computing Applications. Springer Singapore, 2016. http://dx.doi.org/10.1007/978-981-10-2630-0_6.

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Kavya, Duvvuri, Kunisetty Jaswanth, Savarala Chethana, P. Shruti, and Jayan Sarada. "Comparative Analysis of Image Denoising Using Different Filters." In International Conference on Innovative Computing and Communications. Springer Nature Singapore, 2022. http://dx.doi.org/10.1007/978-981-19-2535-1_21.

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Bharati, Subrato, Tanvir Zaman Khan, Prajoy Podder, and Nguyen Quoc Hung. "A Comparative Analysis of Image Denoising Problem: Noise Models, Denoising Filters and Applications." In Cognitive Internet of Medical Things for Smart Healthcare. Springer International Publishing, 2020. http://dx.doi.org/10.1007/978-3-030-55833-8_3.

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Guedj, Benjamin, and Juliette Rengot. "Non-linear Aggregation of Filters to Improve Image Denoising." In Advances in Intelligent Systems and Computing. Springer International Publishing, 2020. http://dx.doi.org/10.1007/978-3-030-52246-9_22.

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Zhao, Huihuang, Dejian Zhou, Shuti Wei, and Yaqi Sun. "SMT Solder Joint Image Denoising Based on Multiple Filters." In 2011 International Conference in Electrics, Communication and Automatic Control Proceedings. Springer New York, 2011. http://dx.doi.org/10.1007/978-1-4419-8849-2_204.

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Shirakol, Shrikanth K., Veerayya Hiremath, and S. S. Kerur. "FPGA-Based Implementation of Digital Filters for Image Denoising." In Lecture Notes in Electrical Engineering. Springer Singapore, 2021. http://dx.doi.org/10.1007/978-981-16-0336-5_14.

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Chen, Ruijie, Xiongxin Tang, Fanjiang Xu, and Chao Yin. "ASFST:Adaptive Spectral Filters Sparse Transformer for Hyperspectral Image Denoising." In Lecture Notes in Computer Science. Springer Nature Singapore, 2025. https://doi.org/10.1007/978-981-96-9856-1_5.

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Siddiqui, Nadia, Syeda Shira Moin, and Saiful Islam. "Analysis of Denoising Filters for Source Identification Using PRNU Features." In Advances in Intelligent Systems and Computing. Springer Singapore, 2019. http://dx.doi.org/10.1007/978-981-13-3600-3_62.

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Qiao, Peng, Weichu Sun, Yong Dou, and Rongchun Li. "Fast Nonlocal Diffusion by Stacking Local Filters for Image Denoising." In Communications in Computer and Information Science. Springer Singapore, 2019. http://dx.doi.org/10.1007/978-981-15-0121-0_6.

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Conference papers on the topic "Denoising Filters"

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Shakeera, S., S. Vazeer Ahamed, V. Bala Naga Jyothi, and H. Venkataraman. "Enhancing Underwater Communication by Dimension Conversion with Denoising Filters." In 2024 IEEE International Conference on Vehicular Electronics and Safety (ICVES). IEEE, 2024. https://doi.org/10.1109/icves61986.2024.10928118.

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Schwarz, Annette, Simon Schmidt, Patrick Wohlfahrt, Jannis Dickmann, and Andreas Maier. "Trainable spatio-temporal bilateral filters: 4D-filtering for 4DCT denoising." In Physics of Medical Imaging, edited by John M. Sabol, Shiva Abbaszadeh, and Ke Li. SPIE, 2025. https://doi.org/10.1117/12.3047389.

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Spagnolo, Fanny, Pasquale Corsonello, Fabio Frustaci, and Stefania Perri. "KIT: Kernel Isotropic Transformation of Bilateral Filters for Image Denoising on FPGA." In 2024 34th International Conference on Field-Programmable Logic and Applications (FPL). IEEE, 2024. http://dx.doi.org/10.1109/fpl64840.2024.00013.

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Jusman, Yessi, Julnila Husna Lubis, Siti Nurul Aqmariah Mohd Kanafiah, et al. "A Comparative Analysis of Denoising Filters on Thalassemia Images in Red Blood Cells." In 2024 9th International Conference on Mechatronics Engineering (ICOM). IEEE, 2024. http://dx.doi.org/10.1109/icom61675.2024.10652459.

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Villa, Jesús, Juan Antonio Quiroga, and Ismael de la Rosa. "Directional filters for fringe pattern denoising." In Seventh Symposium on Optics in Industry, edited by Guillermo García Torales, Jorge L. Flores Núñez, Gilberto Gómez Rosas, and Eric Rosas. SPIE, 2009. http://dx.doi.org/10.1117/12.849339.

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Talebi, Hossein, and Peyman Milanfar. "Improving denoising filters by optimal diffusion." In 2012 19th IEEE International Conference on Image Processing (ICIP 2012). IEEE, 2012. http://dx.doi.org/10.1109/icip.2012.6467076.

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Looney, D., D. P. Mandic, and T. Rutkowski. "Regularised Adaptive Fir Filters for Image Denoising." In 2007 15th International Conference on Digital Signal Processing. IEEE, 2007. http://dx.doi.org/10.1109/icdsp.2007.4288517.

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Joseph, Ardra Mariya, M. Grace John, and Anto Sahaya Dhas. "Mammogram image denoising filters: A comparative study." In 2017 Conference on Emerging Devices and Smart Systems (ICEDSS). IEEE, 2017. http://dx.doi.org/10.1109/icedss.2017.8073679.

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Liu, Z., D. Wheaton, and B. Wang. "Seismic data denoising with mathematical morphological filters." In SEG Technical Program Expanded Abstracts 2019. Society of Exploration Geophysicists, 2019. http://dx.doi.org/10.1190/segam2019-3216392.1.

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Yamini, R., R. Ratha Jeyalakshmi, and R. Sowmini. "PDE based Diffusion Filters for Image Denoising." In 2022 OPJU International Technology Conference on Emerging Technologies for Sustainable Development (OTCON). IEEE, 2023. http://dx.doi.org/10.1109/otcon56053.2023.10114016.

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