Adaptive Order-Statistic LMS Filters

Proceedings of 3rd IEEE International Conference on Image Processing, 1996

Three novel adaptive multichannel L-filters based on marginal data ordering are proposed. They rely on well-known algorithms for the unconstrained minimization of the Mean Squared Error (MSE), namely, the Least Mean Squares (LMS), the normalized LMS (NLMS) and the LMS-Newton (LMSN) algorithm. Performance comparisons in color image filtering have been made both in RGB and U * V * W * color spaces. The proposed adaptive multichannel L-filters outperform the other candidates in noise suppression for color images corrupted by mixed impulsive and additive white contaminated Gaussian noise.

2010 IEEE International Conference on Image Processing, 2010

In this paper, we present a family of order-statistics based vector filters for the removal of impulsive noise from color images. These filters preserve the edges and fine image details by switching between the identity (no filtering) operation and a robust order-statistics based filter operation based on the univariate median operator. Experiments on a diverse set of images and comparisons with state-of-the-art filters show that the proposed filters combine simplicity, flexibility, good filtering quality, and low computational requirements.

1983

International Journal of Remote Sensing, 1996

An algorithm based on local order statistics is proposed for adaptive reduction of speckle noise in synthetic aperture radar (SAR) images. A selective smoothing is obtained by replacing a pixel value belonging to either of the tails of the local histogram by its percentile, whose area is adaptively defined by a Gaussian function of the Local Variation Coefficient. The filter can fit the actual noise level and preserves structures, textures, and point targets, as well as the local mean without introducing any blur on the edges, mostly due to its closure property. Comparisons with algorithms suitable for speckle smoothing are performed on true SAR images and show selective signal-to-noise ratio (SNR) enhancements.

IEEE Transactions on Education, 2004

This paper presents a new approach to introducing adaptive filters based on the least-mean-square (LMS) algorithm and its variants in an undergraduate course on digital signal processing. Unlike other filters currently taught to undergraduate students, these filters are nonlinear and time variant. This proposal introduces adaptive filtering in the context of a linear timeinvariant system using a real problem. In this way, introducing adaptive filters using concepts already familiar to the students motivates their interest through practical application. The key point for this simplification is that the input to the filter is constant so that the adaptive filter becomes linear. Therefore, a complete arsenal of mathematical tools, already known by the students, is available to analyze the performance of the filters and obtain the key parameters to adaptive filters, e.g., speed of convergence and stability. Several variants of the basic LMS algorithm are described the same way.

IEEE Transactions on Signal Processing, 1991

There is a vast number of applications in medical imaging, computer vision, and video communications, where the processing of image sequences is critical. Although many techniques have been successfully developed for tasks such as 3-D motion estimation and image sequence coding, very few techniques have been proposed for the restoration of noisy image sequences. This is due, mainly, to the artifacts introduced when temporal filtering is applied to video containing moving objects. In this paper, we study the application of multistage order statistic filters (MOS) for the task of noise suppression in timevarying imagery. It is shown that MOS filters efficiently preserve image structures under motion without motion compensation preprocessing. In particular, we consider the families of multistage median and weighted median filters. Motion preservation and statistical smoothing measures are derived. Finally, it is shown that spatiotemporal filtering allows for a significant improvement over both spatial and temporal filtering in terms of output image resolution and noise suppression.

Journal of the Optical Society of America A, 2009

Vector operators based on robust order statistics have proved successful in digital multichannel imaging applications, particularly color image filtering and enhancement, in dealing with impulsive noise while preserving edges and fine image details. These operators often have very high computational requirements which limits their use in time-critical applications. This paper introduces techniques to speed up vector filters using the minimax approximation theory. Extensive experiments on a large and diverse set of color images show that proposed approximations achieve an excellent balance among ease of implementation, accuracy, and computational speed.

2015

Many filter design techniques in Digital Signal Processing applications were based on second order statistics which include channel equalization, echo cancellation and system modeling. In these applications filters with adjustable coefficients, called Adaptive Filters were employed. Such Filters incorporate algorithms that allow the filter coefficients to adapt to signal statistics. Adaptive filtering techniques are used in a wide range of applications including echo cancellation, linear prediction, adaptive equalization, adaptive noise cancellation and adaptive beam forming. The design of adaptive filter includes i) Determination of Cost functions like Minimum Square Error (MSE) criterion and exponentially weighted Least Square Error criterion. ii) The performance of adaptive filtering algorithm which depends on the factors like Rate of convergence, misadjustment, tracking capability, computational requirement, and numerical robustness. iii) Structure determination which is inter r...

Journal of Physics: Conference Series, 2016

In recent years significant progress have been made in the development of nonlinear data processing techniques. Such techniques are widely used in digital data filtering and image enhancement. Many of the most effective nonlinear filters based on order statistics. The widely used median filter is the best known order statistic filter. Generalized form of these filters could be presented based on Lloyd's statistics. Filters based on order statistics have excellent robustness properties in the presence of impulsive noise. In this paper, we present special approach for synthesis of order statistics filters using artificial neural networks. Optimal Lloyd's statistics are used for selecting of initial weights for the neural network. Adaptive properties of neural networks provide opportunities to optimize order statistics filters for data with asymmetric distribution function. Different examples demonstrate the properties and performance of presented approach.

2001

A new adaptive multichannel filtering approach is introduced and analyzed in this paper. The technique is simpler and more appropriate than traditional approaches that have been addressed by means of groupwise vector ordering information. These filters are a two-stage filters based on rational functions (RF) using fuzzy transformations of the Euclidean and angular distances among the different vectors to adapt to local data in the color image.