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Добірка наукової літератури з теми "Denoising Image"

Автор: Grafiati

Опубліковано: 6 лютого 2024

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Статті в журналах з теми "Denoising Image"

1

Rubel, Andrii, Oleksii Rubel, Vladimir Lukin, and Karen Egiazarian. "Decision-making on image denoising expedience." Electronic Imaging 2021, no. 10 (January 18, 2021): 237–1. http://dx.doi.org/10.2352/issn.2470-1173.2021.10.ipas-237.

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Анотація:

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 Gaussian noise (AWGN). An algorithm of prediction of subjective image visual quality scores for denoised images using a trained artificial neural network is proposed as well. It is shown that this prediction is fast and accurate.

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2

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

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3

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 (October 24, 2022): 10767. http://dx.doi.org/10.3390/app122110767.

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Анотація:

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 deep image prior (DIP) to enhance the image denoising performance. First, a two-target DIP learning strategy is proposed to impose a learning restriction on the DIP optimization process. A cleaner preliminary image, together with the given noisy image, was used as the learning target of the two-target DIP learning process. We then demonstrate that adding an extra learning target with better image quality in the DIP learning process is capable of constraining the search space of the optimization process and improving the denoising performance. Furthermore, we observe that given the same network input and the same learning target, the DIP optimization process cannot generate the same denoised images. This indicates that the denoised results are uncertain, although they are similar in image quality and are complemented by local details. To utilize the uncertainty of the DIP, we employ a supervised denoising method to preprocess the given noisy image and propose an up- and down-sampling strategy to produce multiple sampled instances of the preprocessed image. These sampled instances were then fed into multiple two-target DIP learning processes to generate multiple denoised instances with different image details. Finally, we propose an unsupervised fusion network that fuses multiple denoised instances into one denoised image to further improve the denoising effect. We evaluated the proposed method through extensive experiments, including grayscale image denoising, color image denoising, and real-world image denoising. The experimental results demonstrate that the proposed framework outperforms unsupervised methods in all cases, and the denoising performance of the framework is close to or superior to that of supervised denoising methods for synthetic noisy image denoising and significantly outperforms supervised denoising methods for real-world image denoising. In summary, the proposed method is essentially a hybrid method that combines both supervised and unsupervised learning to improve denoising performance. Adopting a supervised method to generate preprocessed denoised images can utilize the external prior and help constrict the search space of the DIP, whereas using an unsupervised method to produce intermediate denoised instances can utilize the internal prior and provide adaptability to various noisy images of a real scene.

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4

Huang, Tingsheng, Chunyang Wang, and Xuelian Liu. "Depth Image Denoising Algorithm Based on Fractional Calculus." Electronics 11, no. 12 (June 19, 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 convolution template of this operator is studied and improved upon to build a fractional integral denoising model and algorithm for depth image denoising. Depth images from the Redwood dataset were used to add noise, and the mask constructed by the fractional integral denoising operator was used to denoise the images by convolution. The experimental results show that the fractional integration order with the best denoising effect was −0.4 ≤ ν ≤ −0.3 and that the peak signal-to-noise ratio was improved by +3 to +6 dB. Under the same environment, median filter denoising had −15 to −30 dB distortion. The filtered depth image was converted to a point cloud image, from which the denoising effect was subjectively evaluated. Overall, the results prove that the fractional integral denoising operator can effectively handle noise in depth images while preserving their edge and texture information and thus has an excellent denoising effect.

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5

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

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6

Khan, Aamir, Weidong Jin, Amir Haider, MuhibUr Rahman, and Desheng Wang. "Adversarial Gaussian Denoiser for Multiple-Level Image Denoising." Sensors 21, no. 9 (April 24, 2021): 2998. http://dx.doi.org/10.3390/s21092998.

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Анотація:

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 the generative adversarial network-based adversarial learning process for image denoising tasks. This framework resolves the blurriness problem by encouraging the denoiser network to find the distribution of sharp noise-free images instead of blurry images. Experimental results demonstrate that the proposed framework can effectively resolve the blurriness problem and achieve significant denoising efficiency than the state-of-the-art denoising methods.

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7

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 (November 30, 2022): 1807–14. http://dx.doi.org/10.18280/ts.390540.

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Анотація:

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 using Computed Tomography (CT) to detect liver tumor early, millions of patients could be spared from death each year. Denoising a picture means cleaning up an image that has been corrupted by unwanted noise. Due to the fact that noise, edge, and texture are all high frequency components, denoising can be tricky, and the resulting images may be missing some finer features. Applications where recovering the original image content is vital for good performance benefit greatly from image denoising, including image reconstruction, activity recognition, image restoration, segmentation techniques, and image classification. Tumors of this type are difficult to detect and are almost always discovered at an advanced stage, posing a serious threat to the patient's life. As a result, finding a tumour at an early stage is critical. Tumors can be detected non-invasively using medical image processing. There is a pressing need for software that can automatically read, detect, and evaluate CT scans by removing noise from the images. As a result, any system must deal with a bottleneck in liver segmentation and extraction from CT scans. To segment and classify liver CT images after denoising images, a deep CNN technique is proposed in this research. An Image Quality Enhancement model with Image Denoising and Edge based Segmentation (IQE-ID-EbS) is proposed in this research that effectively reduces noise levels in the image and then performs edge based segmentation for feature extraction from the CT images. The proposed model is compared with the traditional models and the results represent that the proposed model performance is better.

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8

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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Анотація:

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 todenoise the noisy image. In this paper, it is found that the details of original image can be better preserved by combining the prior information of noise and the image edge features to set denoising parameters. The experimental results show that the proposed edge-aware image denoising algorithm can effectively improve the performance of block-matching and 3D filtering and patch group prior-based denoising algorithms and obtain higher peak signal-to-noise ratio and structural similarity values.

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9

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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Анотація:

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 components to perform the denoising process. The proposed algorithm also uses a local Principal Component Analysis decomposition as a postprocessing step to remove more noise by using information not only in the spatial domain but also in the intercomponent domain dealing in a higher noise reduction without significantly affecting the original image resolution. The proposed method has been compared with similar state-of-art methods over synthetic and real clinical multicomponent MR images showing an improved performance in all cases analyzed.

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10

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 (August 31, 2018): 2477–84. http://dx.doi.org/10.31142/ijtsrd18337.

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Дисертації з теми "Denoising Image"

1

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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2

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

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Анотація:

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 fractal and wavelet transforms. In particular, three new image denoising methods are proposed: context-based wavelet thresholding, predictive fractal image denoising and fractal-wavelet image denoising. The proposed context-based thresholding strategy adopts localized hard and soft thresholding operators which take in consideration the content of an immediate neighborhood of a wavelet coefficient before thresholding it. The two fractal-based predictive schemes are based on a simple yet effective algorithm for estimating the fractal code of the original noise-free image from the noisy one. From this predicted code, one can then reconstruct a fractally denoised estimate of the original image. This fractal-based denoising algorithm can be applied in the pixel and the wavelet domains of the noisy image using standard fractal and fractal-wavelet schemes, respectively. Furthermore, the cycle spinning idea was implemented in order to enhance the quality of the fractally denoised estimates. Experimental results show that the proposed image denoising methods are competitive, or sometimes even compare favorably with the existing image denoising techniques reviewed in the thesis. This work broadens the application scope of fractal transforms, which have been used mainly for image coding and compression purposes.

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3

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 segment the smoothed image. For multiplicative noise removal, we employ a difference of convex algorithm to solve the non-convex AA model. And we also improve the non-local total variation model. More precisely, we add an extra term to impose regularity to the graph formed by the weights between pixels. Thin structures can benefit from this regularization term, because it allows to adapt the weights value from the global point of view, thus thin features will not be overlooked like in the conventional non-local models. Since now the non-local total variation term has two variables, the image u and weights v, and it is concave with respect to v, the proximal alternating linearized minimization algorithm is naturally applied with variable metrics to solve the non-convex model efficiently. In the meantime, the efficiency of the proposed approaches is demonstrated on problems including image segmentation, image inpainting and image denoising.

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4

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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Анотація:

Thesis (M.S.)--West Virginia University, 2007.
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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5

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.
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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6

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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7

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 of graphics cards. Today, graphics circuits consist of several hundreds or even thousands of computing units. Using these computing units for general-purpose computation is possible with OpenCL and CUDA programming interfaces. In applications where the processed data is relatively independent, using parallel computing units may increase the performance significantly. Graphics chips enabled with general-purpose computation capabilities are becoming more common also in mobile devices. In addition, photography has never been as popular as it is nowadays by using mobile devices. This thesis aims to implement the calculation of the state-of-the-art technology used in noise reduction, block-matching and three-dimensional filtering (BM3D), to be executed in heterogeneous computing environments. This study evaluates the performance of the presented implementations by making comparisons with existing implementations. The presented implementations achieve significant benefits from the use of parallel computing devices. At the same time the comparisons illustrate general problems in the utilization of using massively parallel processing for the calculation of complex imaging algorithms
Kohinanpoisto on yksi keskeisimmistä digitaaliseen kuvankäsittelyyn liittyvistä ongelmista, joka useimmiten pyritään ratkaisemaan jo signaalinkäsittelyvuon varhaisessa vaiheessa. Kohinaa ilmestyy kuviin monella eri tavalla ja sen esiintyminen on väistämätöntä. Useat kuvankäsittelyalgoritmit toimivat paremmin, jos niiden syöte on valmiiksi mahdollisimman virheetöntä käsiteltäväksi. Jotta kuvankäsittelyviiveet pysyisivät pieninä eri laskenta-alustoilla, on tärkeää että myös kohinanpoisto suoritetaan nopeasti. Viihdeteollisuuden kehityksen myötä näytönohjaimien laskentateho on moninkertaistunut. Nykyisin näytönohjainpiirit koostuvat useista sadoista tai jopa tuhansista laskentayksiköistä. Näiden laskentayksiköiden käyttäminen yleiskäyttöiseen laskentaan on mahdollista OpenCL- ja CUDA-ohjelmointirajapinnoilla. Rinnakkaislaskenta usealla laskentayksiköllä mahdollistaa suuria suorituskyvyn parannuksia käyttökohteissa, joissa käsiteltävä tieto on toisistaan riippumatonta tai löyhästi riippuvaista. Näytönohjainpiirien käyttö yleisessä laskennassa on yleistymässä myös mobiililaitteissa. Lisäksi valokuvaaminen on nykypäivänä suosituinta juuri mobiililaitteilla. Tämä diplomityö pyrkii selvittämään viimeisimmän kohinanpoistoon käytettävän tekniikan, lohkonsovitus ja kolmiulotteinen suodatus (block-matching and three-dimensional filtering, BM3D), laskennan toteuttamista heterogeenisissä laskentaympäristöissä. Työssä arvioidaan esiteltyjen toteutusten suorituskykyä tekemällä vertailuja jo olemassa oleviin toteutuksiin. Esitellyt toteutukset saavuttavat merkittäviä hyötyjä rinnakkaislaskennan käyttämisestä. Samalla vertailuissa havainnollistetaan yleisiä ongelmakohtia näytönohjainlaskennan hyödyntämisessä monimutkaisten kuvankäsittelyalgoritmien laskentaan

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8

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 principe du "global denoising" est reformulé avec le formalisme classique de l'estimation diagonale et son comportement asymptotique est étudié dans le cas oracle. Des conditions précises à la fois sur l'image et sur le filtre global sont introduites pour assurer et quantifier la convergence.Le deuxième chapitre est consacré à l'étude d’a priori gaussiens ou de type mélange de gaussiennes pour le débruitage d'images par patches. Ces a priori sont largement utilisés pour la restauration d'image. Nous proposons ici quelques indices pour répondre aux questions suivantes : Pourquoi ces a priori sont-ils si largement utilisés ? Quelles informations encodent-ils ?Le troisième chapitre propose un modèle probabiliste de mélange pour les patchs bruités, adapté à la grande dimension. Il en résulte un algorithme de débruitage qui atteint les performance de l'état-de-l'art.Le dernier chapitre explore des pistes d'agrégation différentes et propose une écriture de l’étape d'agrégation sous la forme d'un problème de moindre carrés
This thesis studies non-local methods for image processing, and their application to various tasks such as denoising. Natural images contain redundant structures, and this property can be used for restoration purposes. A common way to consider this self-similarity is to separate the image into "patches". These patches can then be grouped, compared and filtered together.In the first chapter, "global denoising" is reframed in the classical formalism of diagonal estimation and its asymptotic behaviour is studied in the oracle case. Precise conditions on both the image and the global filter are introduced to ensure and quantify convergence.The second chapter is dedicated to the study of Gaussian priors for patch-based image denoising. Such priors are widely used for image restoration. We propose some ideas to answer the following questions: Why are Gaussian priors so widely used? What information do they encode about the image?The third chapter proposes a probabilistic high-dimensional mixture model on the noisy patches. This model adopts a sparse modeling which assumes that the data lie on group-specific subspaces of low dimensionalities. This yields a denoising algorithm that demonstrates state-of-the-art performance.The last chapter explores different way of aggregating the patches together. A framework that expresses the patch aggregation in the form of a least squares problem is proposed

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9

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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10

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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Анотація:

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 Aperture Radar (SAR) images. This can be accomplished by comparing these basic methods with well known data adaptive thresholding methods such as SureShrink, BayeShrink, Generalized Cross Validation and Hypothesis Testing. Some nonwavelet denoising process are also introduced. Along with simple mean and median filters, more statistically adaptive median, Lee, Kuan and Frost filtering techniques are also tested to assist wavelet based denoising scheme. All of these methods on the basis of wavelet models and some traditional methods will be implemented in pure java code using plug-in concept of ImageJ which is a popular image processing tool written in Java.

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Книги з теми "Denoising Image"

1

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

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2

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

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3

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

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4

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

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5

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

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6

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

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7

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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Частини книг з теми "Denoising Image"

1

Lisowska, Agnieszka. "Image Denoising." In Geometrical Multiresolution Adaptive Transforms, 67–82. Cham: 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, 273–307. New York, NY: Springer New York, 2010. http://dx.doi.org/10.1007/978-1-4419-7011-4_14.

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3

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, 181–212. Boca Raton: CRC Press, 2023. http://dx.doi.org/10.1201/9781003326830-9.

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4

Gomo, Panganai. "PageRank Image Denoising." In Lecture Notes in Computer Science, 1–10. Berlin, Heidelberg: Springer Berlin Heidelberg, 2010. http://dx.doi.org/10.1007/978-3-642-13772-3_1.

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5

Xiao, Yao, Kai Huang, Hely Lin, and Ruogu Fang. "Medical Imaging Denoising." In Medical Image Synthesis, 99–119. Boca Raton: CRC Press, 2023. http://dx.doi.org/10.1201/9781003243458-10.

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6

Radow, Georg, Michael Breuß, Laurent Hoeltgen, and Thomas Fischer. "Optimised Anisotropic Poisson Denoising." In Image Analysis, 502–14. Cham: Springer International Publishing, 2017. http://dx.doi.org/10.1007/978-3-319-59126-1_42.

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7

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, 769–77. Singapore: Springer Nature Singapore, 2022. http://dx.doi.org/10.1007/978-981-19-2456-9_77.

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Анотація:

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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8

Lisowska, Agnieszka. "Multiwedgelets in Image Denoising." In Lecture Notes in Electrical Engineering, 3–11. Dordrecht: Springer Netherlands, 2013. http://dx.doi.org/10.1007/978-94-007-6738-6_1.

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9

Koziarski, Michał, and Bogusław Cyganek. "Deep Neural Image Denoising." In Computer Vision and Graphics, 163–73. Cham: Springer International Publishing, 2016. http://dx.doi.org/10.1007/978-3-319-46418-3_15.

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10

Kumbhar, Mursal Furqan. "Image Denoising Using Autoencoders." In Artificial Intelligence and Knowledge Processing, 137–44. Boca Raton: CRC Press, 2023. http://dx.doi.org/10.1201/9781003328414-13.

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Тези доповідей конференцій з теми "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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2

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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3

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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4

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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5

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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6

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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Анотація:

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 methods to improve the quality of the retinal fundus images before further processing. By selecting a suitable method for denoising, the image details are not lost as well as the contrast is maintained. Based on the performance of several techniques for denoising fundus images, it is found that the Adaptive Median Filter shows a greater performance compared with state of art methods.

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Насонов, Андрей, Andrey Nasonov, Николай Мамаев, Nikolay Mamaev, Ольга Володина, Olga Volodina, Андрей Крылов, and Andrey Krylov. "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 effectiveness of no-reference parameter choice for the problem of image denoising.

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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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9

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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Звіти організацій з теми "Denoising Image"

1

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

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2

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), November 2019. http://dx.doi.org/10.2172/1592945.

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3

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), April 2020. http://dx.doi.org/10.2172/1617438.

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4

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

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5

Nifong, Nathaniel. Learning General Features From Images and Audio With Stacked Denoising Autoencoders. Portland State University Library, January 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. Fort Belvoir, VA: Defense Technical Information Center, November 2007. http://dx.doi.org/10.21236/ada489758.

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7

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

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