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Yrjö Neuvo - One of the best experts on this subject based on the ideXlab platform.

  • Weighted Median Filters: A tutorial
    IEEE Transactions on Circuits and Systems II: Analog and Digital Signal Processing, 1996
    Co-Authors: Lin Yin, Ruikang Yang, Moncef Gabbouj, Yrjö Neuvo
    Abstract:

    Weighted Median (WM) Filters have the robustness and edge preserving capability of the classical Median filter and resemble linear FIR Filters in certain properties. Furthermore, WM Filters belong to the broad class of nonlinear Filters called stack Filters. This enables the use of the tools developed for the latter class in characterizing and analyzing the behavior and properties of WM Filters, e.g. noise attenuation capability. The fact that WM Filters are threshold functions allows the use of neural network training methods to obtain adaptive WM Filters. In this tutorial paper we trace the development of the theory of WM filtering from its beginnings in the Median filter to the recently developed theory of optimal weighted Median filtering. Applications discussed include: idempotent weighted Median Filters for speech processing, adaptive weighted Median and optimal weighted Median Filters for image and image sequence restoration, weighted Medians as robust predictors in DPCM coding and Quincunx coding, and weighted Median Filters in scan rate conversion in normal TV and HDTV systems

  • ISCAS - Optimal weighted Median Filters under structural constraints
    1993 IEEE International Symposium on Circuits and Systems, 1993
    Co-Authors: Ruikang Yang, Moncef Gabbouj, Jaakko Astola, Yrjö Neuvo
    Abstract:

    An algorithm is developed for finding optimal weighted Median (WM) Filters which minimize noise subject to a predetermined set of structural constraints on the filter's behavior. Based on the derivation of the output moments of weighted Medians, it is shown that optimal weighted Medians with structural constraints may be found by solving a group of linear inequalities. One-dimensional applications are discussed. >

  • ICASSP - Neural Filters: a class of Filters unifying FIR and Median Filters
    [Proceedings] ICASSP-92: 1992 IEEE International Conference on Acoustics Speech and Signal Processing, 1992
    Co-Authors: L. Yin, Jaakko Astola, Yrjö Neuvo
    Abstract:

    A new class of nonlinear Filters called neural Filters based on the threshold decomposition and neural networks is introduced. Neural Filters can approximate both linear finite impulse response (FIR) Filters and weighted order statistic (WOS) Filters which include Median, rank order, and weighted Median Filters. An adaptive algorithm is derived for determining optimal neural Filters under the mean squared error (MSE) criterion. Experimental results demonstrate that, if the input signal is corrupted by Gaussian noise, adaptive neural Filters converge to linear Filters and that, if corrupted by impulsive noise, optimal neural Filters become WOS Filters. >

  • Matched Median filtering
    IEEE Transactions on Communications, 1992
    Co-Authors: Jaakko Astola, Yrjö Neuvo
    Abstract:

    A class of Median type Filters, called matched Median Filters, is defined for the estimation of the information which is carried by the amplitude of a noisy signal. These Filters form the natural counterpart of linear matched Filters in the class of Median type Filters and are maximum likelihood estimators if the noise is biexponential. Matched Median Filters are defined both for baseband and passband systems. Their statistical properties are analyzed and simulation results presented. >

Gonzalo R Arce - One of the best experts on this subject based on the ideXlab platform.

  • weighted Median Filters for multichannel signals
    IEEE Transactions on Signal Processing, 2006
    Co-Authors: Gonzalo R Arce, J Bacca
    Abstract:

    Weighted Medians over multichannel signals are not uniquely defined. Due to its simplicity, Astola 's Vector Median (VM) has received considerable attention particularly in image processing applications. In this paper, we show that the VM and its direct extension the Weighted VM are limited as they do not fully utilize the cross-channel correlation. In fact, VM treats all sub-channel components independent of each other. By revisiting the principles of Maximum Likelihood estimation of location in a multivariate signal space, we propose two new and conceptually simple multichannel weighted Median Filters which can capture cross-channel information effectively. Their optimal filter derivations are also presented, followed by a series of simulations from color image denoising to array signal processing where the advantages of the new filtering structures are illustrated

  • spectral design of weighted Median Filters a general iterative approach
    IEEE Transactions on Signal Processing, 2005
    Co-Authors: Sebastian Hoyos, J Bacca, Gonzalo R Arce
    Abstract:

    A new design strategy for weighted Median (WM) Filters admitting real and complex valued weights is presented. The algorithms are derived from Mallows theory for nonlinear selection type smoothers, which states that the closest linear filter to a selection type smoother in the mean square error sense is the one having as coefficients the sample selection probabilities (SSPs) of the smoother. The new design method overcomes the severe limitations of previous approaches that require the construction of high order polynomial functions and high dimensional matrices. As such, previous approaches could only provide solutions for Filters of very small sizes. The proposed method is based on a new closed-form function used to derive the SSPs of any WM smoother. This function allows for an iterative approach to WM filter design from the spectral profile of a linear filter. This method is initially applied to solve the Median filter design problem in the real domain, and then, it is extended to the complex domain. The final optimization algorithm allows the design of robust weighted Median Filters of arbitrary size based on linear Filters having arbitrary spectral characteristics.

  • recursive weighted Median Filters admitting negative weights and their optimization
    IEEE Transactions on Signal Processing, 2000
    Co-Authors: Gonzalo R Arce, Jose Luis Paredes
    Abstract:

    A recursive weighted Median (RWM) filter structure admitting negative weights is introduced. Much like the sample Median is analogous to the sample mean, the proposed class of RWM Filters is analogous to the class of infinite impulse response (IIR) linear Filters. RWM Filters provide advantages over linear IIR Filters, offering near perfect "stopband" characteristics and robustness against noise. Unlike linear IIR Filters, RWM Filters are always stable under the bounded-input bounded-output criterion, regardless of the values taken by the feedback filter weights. RWM Filters also offer a number of advantages over their nonrecursive counterparts, including a significant reduction in computational complexity, increased robustness to noise, and the ability to model "resonant" or vibratory behavior. A novel "recursive decoupling" adaptive optimization algorithm for the design of this class of recursive WM Filters is also introduced. Several properties of RWM Filters are presented, and a number of simulations are included to illustrate the advantages of RWM Filters over their nonrecursive counterparts and IIR linear Filters.

Jon Yngve Hardeberg - One of the best experts on this subject based on the ideXlab platform.

  • spectral ordering assessment using spectral Median Filters
    International Symposium on Memory Management, 2015
    Co-Authors: Hilda Deborah, Noel Richard, Jon Yngve Hardeberg
    Abstract:

    Distance-based mathematical morphology offers a promising opportunity to develop a metrological spectral image processing framework. Within this objective, a suitable spectral ordering relation is required and it must be validated by metrological means, e.g. accuracy, bias, uncertainty, etc. In this work we address the questions of suitable ordering relation and its uncertainty for the specific case of hyperspectral images. Median filter is shown to be a suitable tool for the assessment of spectral ordering uncertainty. Several spectral ordering relations are provided and the performances of spectral Median Filters based on the aforementioned ordering relations are compared.

  • ISMM - Spectral Ordering Assessment Using Spectral Median Filters
    Lecture Notes in Computer Science, 2015
    Co-Authors: Hilda Deborah, Noel Richard, Jon Yngve Hardeberg
    Abstract:

    Distance-based mathematical morphology offers a promising opportunity to develop a metrological spectral image processing framework. Within this objective, a suitable spectral ordering relation is required and it must be validated by metrological means, e.g. accuracy, bias, uncertainty, etc. In this work we address the questions of suitable ordering relation and its uncertainty for the specific case of hyperspectral images. Median filter is shown to be a suitable tool for the assessment of spectral ordering uncertainty. Several spectral ordering relations are provided and the performances of spectral Median Filters based on the aforementioned ordering relations are compared.

Hilda Deborah - One of the best experts on this subject based on the ideXlab platform.

  • spectral ordering assessment using spectral Median Filters
    International Symposium on Memory Management, 2015
    Co-Authors: Hilda Deborah, Noel Richard, Jon Yngve Hardeberg
    Abstract:

    Distance-based mathematical morphology offers a promising opportunity to develop a metrological spectral image processing framework. Within this objective, a suitable spectral ordering relation is required and it must be validated by metrological means, e.g. accuracy, bias, uncertainty, etc. In this work we address the questions of suitable ordering relation and its uncertainty for the specific case of hyperspectral images. Median filter is shown to be a suitable tool for the assessment of spectral ordering uncertainty. Several spectral ordering relations are provided and the performances of spectral Median Filters based on the aforementioned ordering relations are compared.

  • ISMM - Spectral Ordering Assessment Using Spectral Median Filters
    Lecture Notes in Computer Science, 2015
    Co-Authors: Hilda Deborah, Noel Richard, Jon Yngve Hardeberg
    Abstract:

    Distance-based mathematical morphology offers a promising opportunity to develop a metrological spectral image processing framework. Within this objective, a suitable spectral ordering relation is required and it must be validated by metrological means, e.g. accuracy, bias, uncertainty, etc. In this work we address the questions of suitable ordering relation and its uncertainty for the specific case of hyperspectral images. Median filter is shown to be a suitable tool for the assessment of spectral ordering uncertainty. Several spectral ordering relations are provided and the performances of spectral Median Filters based on the aforementioned ordering relations are compared.

Yangsoo Park - One of the best experts on this subject based on the ideXlab platform.

  • Some root properties of recursive weighted Median Filters
    Signal Processing, 1991
    Co-Authors: Youngok Han, Iickho Song, Yangsoo Park
    Abstract:

    Abstract The weights of weighted Median and recursive weighted Median Filters have close relationship with the root signal characteristics of these Filters. In this paper, we find a set of conditions on weights under which a recursive weighted Median filter preserves monotone or locally monotone sequence and that under which any input sequence converges to a locally monotone sequence after a finite number of passes. We also find a set of conditions on weights under which the output of a recursive weighted Median filter is the same as that of a recursive Median filter.

  • Weight conditions of recursive weighted Median Filters
    [1991] IEEE Pacific Rim Conference on Communications Computers and Signal Processing Conference Proceedings, 1
    Co-Authors: Youngok Han, Iickho Song, Yangsoo Park
    Abstract:

    The authors consider some root properties of recursive weighted Median (RWM) Filters. Specifically, a set of conditions on weights for an RWM filter to preserve monotone or locally monotone sequences is found. The authors also find the weight conditions under which any input sequence will converge to a locally monotone sequence after a finite number of passes and those under which the output of an RWM filter is equivalent to that of an RM filter. >