'Wiener filter' diaporamas de présentation

Speech Segregation

DeLiang Wang Perception & Neurodynamics Lab Ohio State University http://www.cse.ohio-state.edu/pnl/. Speech Segregation. Outline of presentation. Introduction: Speech segregation problem Auditory scene analysis (ASA) Speech enhancement

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Image Processing and Deconvolution

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Image Restoration and Reconstruction (Linear Restoration Methods)

Digital Image Processing. Image Restoration and Reconstruction (Linear Restoration Methods). Christophoros Nikou cnikou@cs.uoi.gr. Contents. In this lecture we will look at linear image restoration techniques Differentiation of matrices and vectors Linear space invariant degradation

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Chapter 8: Image Restoration

Chapter 8: Image Restoration. Image enhancement: Overlook degradation processes, deal with images intuitively Image restoration: Known degradation processes; model the processes and reconstruct images based on the inverse model ? Degradations

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Antoine O. Berthet (1) , Raphael Visoz (2) , Sami Chtourou (2)

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Despeckle Filtering in Medical Ultrasound Imaging

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Sampling Random Signals

Sampling Random Signals. Introduction Types of Priors. Subspace priors:. Smoothness priors:. Stochastic priors:. Introduction Motivation for Stochastic Modeling. Understanding of artifacts via stationarity analysis New scheme for constrained reconstruction Error analysis.

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Real time DSP

Real time DSP. Professors: Eng. Julian Bruno Eng. Mariano Llamedo Soria. Adaptive Filters. Recommended bibliography. Saeed V. Vaseghi, Advanced Digital Signal Processing and Noise Reduction , Second Edition. John Wiley & Sons Ltd. Ch 3: Probabilistic Models Ch 6: Wiener Filters

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METHOD OF STEEPEST DESCENT

METHOD OF STEEPEST DESCENT. Mean Square Error (Revisited). For a transversal filter (of length M), the output is written as and the error term wrt. a certain desired response is . Mean Square Error (Revisited). Following these terms, the MSE criterion is defined as

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Chapter 6

Chapter 6. Image Restoration Digital Image Processing Instructor: Dr. Cheng-Chien Liu Department of Earth Sciences National Cheng Kung University Last updated: 10 October 2003. Introduction. Image restoration Use objective criteria and prior knowledge

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Unified Loop Filter for High-performance Video Coding

Unified Loop Filter for High-performance Video Coding. Yu Liu and Yan Huo ICME2010, July 19-23, Singapore. Outline. Introduction Proposed Unified Loop Filter Experimental Results Conclusion. Introduction. Conventional Video Coding Standard Block-based DPCM coding

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Correntropy as a similarity measure

Correntropy as a similarity measure. Computational NeuroEngineering Laboratory University of Florida http://www.cnel.ufl.edu weifeng@cnel.ufl.edu Acknowledgment: This work was partially supported by NSF grant ECS-0300340 and ECS-0601271. Weifeng Liu, P. P. Pokharel, Jose Principe. Outline.

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2009.10.22

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Enhancement of Speech in Noisy Conditions Project Presentation Paul Coffey

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Adaptive Noise Cancellation: A Panacea

Adaptive Noise Cancellation: A Panacea. Evans, R. Adhikari. The Concept. 5000 Channels 1 GW Channel Signal = DARM - noise. Wiener Filter. Norbert Wiener, MIT. Block Toeplitz. Cross Correlation Matrix. Input Signal (PEM) Covariance Matrix. Wiener Filter Sim. H1. L1.

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Digital Image Processing ECE.09.452/ECE.09.552 Fall 2007

Digital Image Processing ECE.09.452/ECE.09.552 Fall 2007. Lecture 5 October 15, 2007. Shreekanth Mandayam ECE Department Rowan University http://engineering.rowan.edu/~shreek/fall07/dip/. Plan. Image Spectrum (Recall) 2-D Fourier Transform (DFT & FFT) Spectral Filtering

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Digital Image Processing 0909.452.01/0909.552.01 Fall 2003

Digital Image Processing 0909.452.01/0909.552.01 Fall 2003. Lecture 6 October 13, 2003. Shreekanth Mandayam ECE Department Rowan University http://engineering.rowan.edu/~shreek/fall03/dip/. Plan. Digital Image Restoration Recall: Environmental Models Image Degradation Model

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7.3.3 Causal Wiener Filter

7.3.3 Causal Wiener Filter. Causal ?? , ??? ??? ??? ??? ??? ??? ??? ??? , ??? ??? ???? ??? ???? ???. Wiener Filter Signal ? noise ? spectral characteristics or autocorrelation, crosscorrelation ? ?? ?? random processes ?? . 2. filter ??? minimum mean-square error ? ???? .

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Project Impact

d 0. ? 0. +. ?. d 1. -. H 1. +. ? 1. X 0. Scalar Weight w 1. ?. -. Weight vector W 2. B 1. X 1. Using a Multi-Stage Wiener Filter for SAR Image Formation in a Distributed Aperture Radar System PI: Dr. Jim Stiles jstiles@rsl.ku.edu GRA: Peng Seng,Tan ptan@ittc.ku.edu.

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