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Relevant bibliographies by topics / Denoising Image

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Academic literature on the topic 'Denoising Image'

Author: Grafiati

Published: 6 February 2024

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Journal articles on the topic "Denoising Image"

1

Rubel, Andrii, Oleksii Rubel, Vladimir Lukin, and Karen Egiazarian. "Decision-making on image denoising expedience." Electronic Imaging 2021, no. 10 (2021): 237–1. http://dx.doi.org/10.2352/issn.2470-1173.2021.10.ipas-237.

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Image denoising is a classical preprocessing stage used to enhance images. However, it is well known that there are many practical cases where different image denoising methods produce images with inappropriate visual quality, which makes an application of image denoising useless. Because of this, it is desirable to detect such cases in advance and decide how expedient is image denoising (filtering). This problem for the case of wellknown BM3D denoiser is analyzed in this paper. We propose an algorithm of decision-making on image denoising expedience for images corrupted by additive white Gaus

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R. Tripathi, Mr Vijay. "Image Denoising." IOSR Journal of Engineering 1, no. 1 (2011): 84–87. http://dx.doi.org/10.9790/3021-0118487.

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Xu, Shaoping, Xiaojun Chen, Yiling Tang, Shunliang Jiang, Xiaohui Cheng, and Nan Xiao. "Learning from Multiple Instances: A Two-Stage Unsupervised Image Denoising Framework Based on Deep Image Prior." Applied Sciences 12, no. 21 (2022): 10767. http://dx.doi.org/10.3390/app122110767.

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Supervised image denoising methods based on deep neural networks require a large amount of noisy-clean or noisy image pairs for network training. Thus, their performance drops drastically when the given noisy image is significantly different from the training data. Recently, several unsupervised learning models have been proposed to reduce the dependence on training data. Although unsupervised methods only require noisy images for learning, their denoising effect is relatively weak compared with supervised methods. This paper proposes a two-stage unsupervised deep learning framework based on d

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Huang, Tingsheng, Chunyang Wang, and Xuelian Liu. "Depth Image Denoising Algorithm Based on Fractional Calculus." Electronics 11, no. 12 (2022): 1910. http://dx.doi.org/10.3390/electronics11121910.

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Depth images are often accompanied by unavoidable and unpredictable noise. Depth image denoising algorithms mainly attempt to fill hole data and optimise edges. In this paper, we study in detail the problem of effectively filtering the data of depth images under noise interference. The classical filtering algorithm tends to blur edge and texture information, whereas the fractional integral operator can retain more edge and texture information. In this paper, the Grünwald–Letnikov-type fractional integral denoising operator is introduced into the depth image denoising process, and the convoluti

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Bertalmío, Marcelo, and Stacey Levine. "Denoising an Image by Denoising Its Curvature Image." SIAM Journal on Imaging Sciences 7, no. 1 (2014): 187–211. http://dx.doi.org/10.1137/120901246.

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Khan, Aamir, Weidong Jin, Amir Haider, MuhibUr Rahman, and Desheng Wang. "Adversarial Gaussian Denoiser for Multiple-Level Image Denoising." Sensors 21, no. 9 (2021): 2998. http://dx.doi.org/10.3390/s21092998.

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Image denoising is a challenging task that is essential in numerous computer vision and image processing problems. This study proposes and applies a generative adversarial network-based image denoising training architecture to multiple-level Gaussian image denoising tasks. Convolutional neural network-based denoising approaches come across a blurriness issue that produces denoised images blurry on texture details. To resolve the blurriness issue, we first performed a theoretical study of the cause of the problem. Subsequently, we proposed an adversarial Gaussian denoiser network, which uses th

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Gavini, Venkateswarlu, and Gurusamy Ramasamy Jothi Lakshmi. "CT Image Denoising Model Using Image Segmentation for Image Quality Enhancement for Liver Tumor Detection Using CNN." Traitement du Signal 39, no. 5 (2022): 1807–14. http://dx.doi.org/10.18280/ts.390540.

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Image denoising is an important concept in image processing for improving the image quality. It is difficult to remove noise from images because of the various causes of noise. Imaging noise is made up of many different types of noise, including Gaussian, impulse, salt, pepper, and speckle noise. Increasing emphasis has been paid to Convolution Neural Networks (CNNs) in image denoising. Image denoising has been researched using a variety of CNN approaches. For the evaluation of these methods, various datasets were utilized. Liver Tumor is the leading cause of cancer-related death worldwide. By

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Zhang, Xiangning, Yan Yang, and Lening Lin. "Edge-aware image denoising algorithm." Journal of Algorithms & Computational Technology 13 (October 30, 2018): 174830181880477. http://dx.doi.org/10.1177/1748301818804774.

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The key of image denoising algorithms is to preserve the details of the original image while denoising the noise in the image. The existing algorithms use the external information to better preserve the details of the image, but the use of external information needs the support of similar images or image patches. In this paper, an edge-aware image denoising algorithm is proposed to achieve the goal of preserving the details of original image while denoising and using only the characteristics of the noisy image. In general, image denoising algorithms use the noise prior to set parameters todeno

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Manjón, José V., Neil A. Thacker, Juan J. Lull, Gracian Garcia-Martí, Luís Martí-Bonmatí, and Montserrat Robles. "Multicomponent MR Image Denoising." International Journal of Biomedical Imaging 2009 (2009): 1–10. http://dx.doi.org/10.1155/2009/756897.

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Magnetic Resonance images are normally corrupted by random noise from the measurement process complicating the automatic feature extraction and analysis of clinical data. It is because of this reason that denoising methods have been traditionally applied to improve MR image quality. Many of these methods use the information of a single image without taking into consideration the intrinsic multicomponent nature of MR images. In this paper we propose a new filter to reduce random noise in multicomponent MR images by spatially averaging similar pixels using information from all available image co

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Badgainya, Shruti, Prof Pankaj Sahu, and Prof Vipul Awasthi. "Image Denoising by OWT for Gaussian Noise Corrupted Images." International Journal of Trend in Scientific Research and Development Volume-2, Issue-5 (2018): 2477–84. http://dx.doi.org/10.31142/ijtsrd18337.

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Dissertations / Theses on the topic "Denoising Image"

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Zhang, Jiachao. "Image denoising for real image sensors." University of Dayton / OhioLINK, 2015. http://rave.ohiolink.edu/etdc/view?acc_num=dayton1437954286.

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Ghazel, Mohsen. "Adaptive Fractal and Wavelet Image Denoising." Thesis, University of Waterloo, 2004. http://hdl.handle.net/10012/882.

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The need for image enhancement and restoration is encountered in many practical applications. For instance, distortion due to additive white Gaussian noise (AWGN) can be caused by poor quality image acquisition, images observed in a noisy environment or noise inherent in communication channels. In this thesis, image denoising is investigated. After reviewing standard image denoising methods as applied in the spatial, frequency and wavelet domains of the noisy image, the thesis embarks on the endeavor of developing and experimenting with new image denoising methods based on fra

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Li, Zhi. "Variational image segmentation, inpainting and denoising." HKBU Institutional Repository, 2016. https://repository.hkbu.edu.hk/etd_oa/292.

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Variational methods have attracted much attention in the past decade. With rigorous mathematical analysis and computational methods, variational minimization models can handle many practical problems arising in image processing, such as image segmentation and image restoration. We propose a two-stage image segmentation approach for color images, in the first stage, the primal-dual algorithm is applied to efficiently solve the proposed minimization problem for a smoothed image solution without irrelevant and trivial information, then in the second stage, we adopt the hillclimbing procedure to s

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Danda, Swetha. "Generalized diffusion model for image denoising." Morgantown, W. Va. : [West Virginia University Libraries], 2007. https://eidr.wvu.edu/etd/documentdata.eTD?documentid=5481.

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Thesis (M.S.)--West Virginia University, 2007.<br>Title from document title page. Document formatted into pages; contains viii, 62 p. : ill. Includes abstract. Includes bibliographical references (p. 59-62).

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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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Hussain, Israr. "Non-gaussianity based image deblurring and denoising." Thesis, University of Manchester, 2008. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.489022.

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Sarjanoja, S. (Sampsa). "BM3D image denoising using heterogeneous computing platforms." Master's thesis, University of Oulu, 2015. http://urn.fi/URN:NBN:fi:oulu-201504141380.

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Noise reduction is one of the most fundamental digital image processing problems, and is often designed to be solved at an early stage of the image processing path. Noise appears on the images in many different ways, and it is inevitable. In general, various image processing algorithms perform better if their input is as error-free as possible. In order to keep the processing delays small in different computing platforms, it is important that the noise reduction is performed swiftly. The recent progress in the entertainment industry has led to major improvements in the computing capabilities o

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Houdard, Antoine. "Some advances in patch-based image denoising." Thesis, Université Paris-Saclay (ComUE), 2018. http://www.theses.fr/2018SACLT005/document.

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Cette thèse s'inscrit dans le contexte des méthodes non locales pour le traitement d'images et a pour application principale le débruitage, bien que les méthodes étudiées soient suffisamment génériques pour être applicables à d'autres problèmes inverses en imagerie. Les images naturelles sont constituées de structures redondantes, et cette redondance peut être exploitée à des fins de restauration. Une manière classique d’exploiter cette auto-similarité est de découper l'image en patchs. Ces derniers peuvent ensuite être regroupés, comparés et filtrés ensemble.Dans le premier chapitre, le princ

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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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Tuncer, Guney. "A Java Toolbox For Wavelet Based Image Denoising." Master's thesis, METU, 2005. http://etd.lib.metu.edu.tr/upload/12608037/index.pdf.

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Wavelet methods for image denoising have became widespread for the last decade. The effectiveness of this denoising scheme is influenced by many factors. Highlights can be listed as choosing of wavelet used, the threshold determination and transform level selection for thresholding. For threshold calculation one of the classical solutions is Wiener filter as a linear estimator. Another one is VisuShrink using global thresholding for nonlinear area. The purpose of this work is to develop a Java toolbox which is used to find best denoising schemes for distinct image types particularly Synthetic

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Books on the topic "Denoising Image"

1

Shukla, K. K. Efficient Algorithms for Discrete Wavelet Transform: With Applications to Denoising and Fuzzy Inference Systems. Springer London, 2013.

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2

Bertalmío, Marcelo, ed. Denoising of Photographic Images and Video. Springer International Publishing, 2018. http://dx.doi.org/10.1007/978-3-319-96029-6.

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Kok, Chi-Wah, and Wing-Shan Tam. Digital Image Denoising in MATLAB. Wiley & Sons, Incorporated, John, 2022.

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Kok, Chi-Wah, and Wing-Shan Tam. Digital Image Denoising in MATLAB. Wiley & Sons, Incorporated, John, 2022.

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Kok, Chi-Wah, and Wing-Shan Tam. Digital Image Denoising in MATLAB. Wiley & Sons, Limited, John, 2022.

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Kok, Chi-Wah, and Wing-Shan Tam. Digital Image Denoising in MATLAB. Wiley & Sons, Incorporated, John, 2022.

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Shukla, K. K., and Arvind K. Tiwari. Efficient Algorithms for Discrete Wavelet Transform: With Applications to Denoising and Fuzzy Inference Systems. Springer London, Limited, 2013.

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8

Bertalmío, Marcelo. Denoising of Photographic Images and Video: Fundamentals, Open Challenges and New Trends. Springer, 2018.

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9

Bertalmío, Marcelo. Denoising of Photographic Images and Video: Fundamentals, Open Challenges and New Trends. Springer, 2018.

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Book chapters on the topic "Denoising Image"

1

Lisowska, Agnieszka. "Image Denoising." In Geometrical Multiresolution Adaptive Transforms. Springer International Publishing, 2014. http://dx.doi.org/10.1007/978-3-319-05011-9_6.

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2

Elad, Michael. "Image Denoising." In Sparse and Redundant Representations. Springer New York, 2010. http://dx.doi.org/10.1007/978-1-4419-7011-4_14.

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Aravind, B. N., K. V. Suresh, Nataraj H. D. Urs, N. Yashwanth, and Usha Desai. "Image Denoising." In Human-Machine Interface Technology Advancements and Applications. CRC Press, 2023. http://dx.doi.org/10.1201/9781003326830-9.

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Gomo, Panganai. "PageRank Image Denoising." In Lecture Notes in Computer Science. Springer Berlin Heidelberg, 2010. http://dx.doi.org/10.1007/978-3-642-13772-3_1.

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Xiao, Yao, Kai Huang, Hely Lin, and Ruogu Fang. "Medical Imaging Denoising." In Medical Image Synthesis. CRC Press, 2023. http://dx.doi.org/10.1201/9781003243458-10.

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Radow, Georg, Michael Breuß, Laurent Hoeltgen, and Thomas Fischer. "Optimised Anisotropic Poisson Denoising." In Image Analysis. Springer International Publishing, 2017. http://dx.doi.org/10.1007/978-3-319-59126-1_42.

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Zhang, Jiangang, Xiang Pan, and Tianxu Lv. "Unsupervised MRI Images Denoising via Decoupled Expression." In Proceeding of 2021 International Conference on Wireless Communications, Networking and Applications. Springer Nature Singapore, 2022. http://dx.doi.org/10.1007/978-981-19-2456-9_77.

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AbstractMagnetic Resonance Imaging (MRI) is widely adopted in medical diagnosis. Due to the spatial coding scheme, MRI image is degraded by various noise. Recently, massive methods have been applied to the MRI image denoising. However, they lack the consideration of artifacts in MRI images. In this paper, we propose an unsupervised MRI image denoising method called UEGAN based on decoupled expression. We decouple the content and noise in a noisy image using content encoders and noise encoders. We employ a noising branch to push the noise decoder only extract the noise. The cycle-consistency loss ensures that the content of the denoised results match the original images. To acquire visually realistic generations, we add an adversarial loss on denoised results. Image quality penalty helps to retain rich image details. We perform experiments on unpaired MRI images from Brainweb datesets, and achieve superior performances compared to several popular denoising approaches.

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Lisowska, Agnieszka. "Multiwedgelets in Image Denoising." In Lecture Notes in Electrical Engineering. Springer Netherlands, 2013. http://dx.doi.org/10.1007/978-94-007-6738-6_1.

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Koziarski, Michał, and Bogusław Cyganek. "Deep Neural Image Denoising." In Computer Vision and Graphics. Springer International Publishing, 2016. http://dx.doi.org/10.1007/978-3-319-46418-3_15.

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Kumbhar, Mursal Furqan. "Image Denoising Using Autoencoders." In Artificial Intelligence and Knowledge Processing. CRC Press, 2023. http://dx.doi.org/10.1201/9781003328414-13.

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Conference papers on the topic "Denoising Image"

1

Yue, Huanjing, Xiaoyan Sun, Jingyu Yang, and Feng Wu. "Image denoising using cloud images." In SPIE Optical Engineering + Applications, edited by Andrew G. Tescher. SPIE, 2013. http://dx.doi.org/10.1117/12.2022506.

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Estrada, Francisco, David Fleet, and Allan Jepson. "Stochastic Image Denoising." In British Machine Vision Conference 2009. British Machine Vision Association, 2009. http://dx.doi.org/10.5244/c.23.117.

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Liu, Yang, Saeed Anwar, Liang Zheng, and Qi Tian. "GradNet Image Denoising." In 2020 IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops (CVPRW). IEEE, 2020. http://dx.doi.org/10.1109/cvprw50498.2020.00262.

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Aravind, B. N., and K. V. Suresh. "Hybrid image denoising." In 2017 International Conference on Electrical, Electronics, Communication, Computer, and Optimization Techniques (ICEECCOT). IEEE, 2017. http://dx.doi.org/10.1109/iceeccot.2017.8284524.

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Kattakinda, Priyatham, and A. N. Rajagopalan. "Unpaired Image Denoising." In 2020 IEEE International Conference on Image Processing (ICIP). IEEE, 2020. http://dx.doi.org/10.1109/icip40778.2020.9190932.

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S. B, Anuja, and Ramesh Dhanaseelan F. "Denoising of Diabetic Retinopathy Images Using Adaptive Median Filter." In The International Conference on scientific innovations in Science, Technology, and Management. International Journal of Advanced Trends in Engineering and Management, 2023. http://dx.doi.org/10.59544/gxpd6690/ngcesi23p15.

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One of the fundamental challenges in the field of Image Processing and Computer Vision is Image Denoising. The goal is to estimate the original image by removing or suppressing noise from a noise-contaminated version of the image. Noise can be introduced into an image during acquisition, processing, or transmission which can reduce the image quality and make it difficult to interpret. Diagnosing retinal diseases of the eye requires analyzing tiny retinal vessels. The digital color present in the images, and retinal vasculature is difficult to be analyzed. This paper discusses various denoising

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Насонов, Андрей, Andrey Nasonov, Николай Мамаев, et al. "Automatic Choice of Denoising Parameter in Perona-Malik Model." In 29th International Conference on Computer Graphics, Image Processing and Computer Vision, Visualization Systems and the Virtual Environment GraphiCon'2019. Bryansk State Technical University, 2019. http://dx.doi.org/10.30987/graphicon-2019-2-144-147.

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In this work, we propose a no-reference method for automatic choice of the parameters of Perona-Malik image diffusion algorithm for the problem of image denoising. The idea of the approach it to analyze and quantify the presence of structures in the difference image between the noisy image and the processed image as the mutual information value. We apply the proposed method to photographic images and to retinal images with modeled Gaussian noise with different parameters and analyze the effects of no-reference parameter choice compared to the optimal results. The proposed algorithm shows the e

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Gondara, Lovedeep. "Medical Image Denoising Using Convolutional Denoising Autoencoders." In 2016 IEEE 16th International Conference on Data Mining Workshops (ICDMW). IEEE, 2016. http://dx.doi.org/10.1109/icdmw.2016.0041.

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Xiang, Qian, and Xuliang Pang. "Improved Denoising Auto-Encoders for Image Denoising." In 2018 11th International Congress on Image and Signal Processing, BioMedical Engineering and Informatics (CISP-BMEI). IEEE, 2018. http://dx.doi.org/10.1109/cisp-bmei.2018.8633143.

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Jain, Arti, and Anand Singh Jalal. "An Effective Image Denoising Approach Based on Denoising with Image Interpolation." In 2023 IEEE World Conference on Applied Intelligence and Computing (AIC). IEEE, 2023. http://dx.doi.org/10.1109/aic57670.2023.10263909.

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Reports on the topic "Denoising Image"

1

Yufang, Bao. Nonlinear Image Denoising Methodologies. Defense Technical Information Center, 2002. http://dx.doi.org/10.21236/ada460128.

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D'Elia, Marta, and De lo Reyes, Juan Carlos, Miniguano, Andres. Bilevel parameter optimization for nonlocal image denoising models. Office of Scientific and Technical Information (OSTI), 2019. http://dx.doi.org/10.2172/1592945.

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D'Elia, Marta, Juan Carlos De los Reyes, and Andres Trujillo. Bilevel parameter optimization for learning nonlocal image denoising models. Office of Scientific and Technical Information (OSTI), 2020. http://dx.doi.org/10.2172/1617438.

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Potts, Catherine Gabriel. Visual Data: Technical Diagrams. Denoising of Technical Diagram Images. Office of Scientific and Technical Information (OSTI), 2019. http://dx.doi.org/10.2172/1558025.

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Nifong, Nathaniel. Learning General Features From Images and Audio With Stacked Denoising Autoencoders. Portland State University Library, 2000. http://dx.doi.org/10.15760/etd.1549.

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Tadmor, Eitan, Suzanne Nezzar, and Luminita Vese. Multiscale Hierarchical Decomposition of Images with Applications to Deblurring, Denoising and Segmentation. Defense Technical Information Center, 2007. http://dx.doi.org/10.21236/ada489758.

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Levesque, Joseph. Neural network denoising of HED x-ray images, with an introduction to neural networks. Office of Scientific and Technical Information (OSTI), 2023. http://dx.doi.org/10.2172/1970268.

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