Image denoising

Xem 1-19 trên 19 kết quả Image denoising
  • There are six sections in this book. The first section presents basic image processing techniques, such as image acquisition, storage, retrieval, transformation, filtering, and parallel computing. Then, some applications, such as road sign recognition, air quality monitoring, remote sensed image analysis, and diagnosis of industrial parts are considered.

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  • Tuyển tập báo cáo các nghiên cứu khoa học quốc tế ngành hóa học dành cho các bạn yêu hóa học tham khảo đề tài: Image denoising by a direct variational minimization

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  • Digital image processing is an important research area. The techniques developed in this area so far require to be summarized in an appropriate way. In this book, the fundamental theories of these techniques will be introduced. Particularly, their applications in image denoising, restoration, and segmentation will be introduced. The entire book consists of four chapters, which will be subsequently introduced.

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  • Tham khảo sách 'image processing_1', công nghệ thông tin, kỹ thuật lập trình phục vụ nhu cầu học tập, nghiên cứu và làm việc hiệu quả

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  • Tuyển tập báo cáo các nghiên cứu khoa học quốc tế ngành hóa học dành cho các bạn yêu hóa học tham khảo đề tài: Research Article Iterative Estimation Algorithms Using Conjugate Function Lower Bound and Minorization-Maximization with Applications in Image Denoising

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  • Tuyển tập báo cáo các nghiên cứu khoa học quốc tế ngành hóa học dành cho các bạn yêu hóa học tham khảo đề tài: A Multivariate Thresholding Technique for Image Denoising Using Multiwavelets

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  • Discrete wavelet transform (DWT) algorithms have become standard tools for discrete-time signal and image processing in several areas in research and industry. As DWT provides both frequency and location information of the analyzed signal, it is constantly used to solve and treat more and more advanced problems. The present book: Discrete Wavelet Transforms: Theory and Applications describes the latest progress in DWT analysis in non-stationary signal processing, multi-scale image enhancement as well as in biomedical and industrial applications....

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  • Discrete Wavelet Transform is a wavelet (DWT) transform that is widely used in numerical and functional analysis. Its key advantage over more traditional transforms, such as the Fourier transform, lies in its ability to offer temporal resolution, i.e. it captures both frequency and location (or time) information.

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  • Tuyển tập báo cáo các nghiên cứu khoa học quốc tế ngành hóa học dành cho các bạn yêu hóa học tham khảo đề tài: A scale-based forward-and-backward diffusion process for adaptive image enhancement and denoising

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  • Tuyển tập các báo cáo nghiên cứu về hóa học được đăng trên tạp chí sinh học đề tài : Rician nonlocal means denoising for MR images using nonparametric principal component analysis

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  • Tuyển tập các báo cáo nghiên cứu về sinh học được đăng trên tạp chí sinh học Journal of Biology đề tài: Research Article Image Variational Denoising Using Gradient Fidelity on Curvelet Shrinkage

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  • Tuyển tập báo cáo các nghiên cứu khoa học quốc tế ngành hóa học dành cho các bạn yêu hóa học tham khảo đề tài:Research Article A New Denoising System for SONAR Images

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  • Moreover, the concept of \multimedia social networks" can be applied into the ¯eld of signal and image processing. If each pixel/sample is treated as a user, then the whole image/signal can be regarded as a multimedia social network. From such a perspective, we introduce a new paradigm for signal and image processing, and develop generalized and uni¯ed frameworks for classical signal and image prob- lems. In this thesis, we use image denoising and image interpolation as examples to illustrate how to use game theory to re-formulate the classical signal and image processing problems....

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  • This textbook introduces sparse and redundant representations with a focus on applications in signal and image processing. The theoretical and numerical foundations are tackled before the applications are discussed. Mathematical modeling for signal sources is discussed along with how to use the proper model for tasks such as denoising, restoration, separation, interpolation and extrapolation, compression, sampling, analysis and synthesis, detection, recognition, and more. The presentation is elegant and engaging.

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  • A fundamental approach in signal processing is to design a statistical generative model of the observed signals. The components in the generative model then give a representation of the data. Such a representation can then be used in such tasks as compression, denoising, and pattern recognition. This approach is also useful from a neuroscientific viewpoint, for modeling the properties of neurons in primary sensory areas. In this chapter, we consider a certain class of widely used signals, which we call natural images....

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  • The recent wide diffusion of (i) easy-to-use software tools for the implementation of Graphical User Interfaces (GUIs); and (ii) communication-oriented instrumentation, often provided with Ethernet interface, in addition to the more traditional GPIB and RS-232 ones, can be particularly exploited in the field of measurement teaching. It is well known, in fact, that for a better understanding of the teaching issues in such a field, the students have to practice with real instrumentation. The computer-based simulations are often inadequate to assure a good experience in that direction.

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  • EURASIP Journal on Applied Signal Processing 2003:5, 470–478 c 2003 Hindawi Publishing Corporation Speckle Suppression in Ultrasonic Images Based on Undecimated Wavelets Fabrizio Argenti Dipartimento di Elettronica e Telecomunicazioni, Universit` di Firenze, Via di Santa Marta 3, 50139 Firenze, Italy a Email: argenti@lenst.det.unifi.it Gionatan Torricelli Dipartimento di Elettronica e Telecomunicazioni, Universit` di Firenze, Via di Santa Marta, 3, 50139 Firenze, Italy a Email: torricelli@det.unifi.

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  • EURASIP Journal on Applied Signal Processing 2003:5, 430–436 c 2003 Hindawi Publishing Corporation An Approach to Adaptive Enhancement of Diagnostic X-Ray Images ¨ Hakan Oktem Institute of Signal Processing, Tampere University of Technology, P.O. Box 553, 33101 Tampere, Finland Email: oktem@cs.tut.fi Karen Egiazarian Institute of Signal Processing, Tampere University of Technology, P.O. Box 553, 33101 Tampere, Finland Email: karen@cs.tut.fi Jarkko Niittylahti Atostek Ltd., Hermiankatu 8D, FIN-33720 Tampere, Finland Email: jarkko.niittylahti@atostek.com Juha Lemmetti Atostek Ltd.

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  • Moreover, the concept of \multimedia social networks" can be applied into the ¯eld of signal and image processing. Although there are seemingly no human factors involved in the algorithmic solution in classical signal/image processing, if we take the view that the pixels/signals of an image are forming a notion of a \social network" to jointly interact to accomplish a common (\processing") goal, be it ¯ltering, denoising, or segmentation, then the game theoretic approach can o®er new views beyond what classical methods can.

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