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Journal ArticleDOI

LUM filters: a class of rank-order-based filters for smoothing and sharpening

TLDR
Lower-upper-middle (LUM) filters as mentioned in this paper are a class of rank-order-based filters, which can be designed for smoothing and sharpening, or outlier rejection.
Abstract
A new class of rank-order-based filters, called lower-upper-middle (LUM) filters, is introduced. The output of these filters is determined by comparing a lower- and an upper-order statistic to the middle sample in the filter window. These filters can be designed for smoothing and sharpening, or outlier rejection. The level of smoothing done by the filter can range from no smoothing to that of the median filter. This flexibility allows the LUM filter to be designed to best balance the tradeoffs between noise smoothing and signal detail preservation. LUM filters for enhancing edge gradients can be designed to be insensitive to low levels of additive noise and to remove impulsive noise. Furthermore, LUM filters do not cause overshoot or undershoot. Some statistical and deterministic properties of the LUM filters are developed, and a number of experimental results are presented to illustrate the performance. These experiments include applications to 1D signals and to images. >

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Citations
More filters
Journal ArticleDOI

Review article: Edge and line oriented contour detection: State of the art

TL;DR: The main conclusion is that contour detection has reached high degree of sophistication, taking into account multimodal contour definition (by luminance, color or texture changes), mechanisms for reducing the contour masking influence of noise and texture, perceptual grouping, multiscale aspects and high-level vision information.
Journal ArticleDOI

A fuzzy impulse noise detection and reduction method

TL;DR: A new algorithm that is especially developed for reducing all kinds of impulse noise: fuzzy impulse noise detection and reduction method (FIDRM), which can also be applied to images having a mixture of impulse Noise and other types of noise.
Book ChapterDOI

Image Noise Models

TL;DR: In this article, it is shown that additive Gaussian noise is the limiting behavior of other noises, e.g., photon counting noise and film grain noise, which is a part of almost any signal.
Journal ArticleDOI

Rank conditioned rank selection filters for signal restoration

TL;DR: A class of nonlinear filters called rank conditioned rank selection (RCRS) filters is developed and analyzed and extensive computer simulation results that illustrate the performance of RCRS filters in comparison with other techniques in image restoration applications are presented.
Journal ArticleDOI

Fuzzy random impulse noise reduction method

TL;DR: A new two-step fuzzy filter that adopts a fuzzy logic approach for the enhancement of images corrupted with impulse noise is presented and it is found experimentally that the proposed method provides a significant improvement on other state-of-the-art methods.
References
More filters
Journal ArticleDOI

Center weighted median filters and their applications to image enhancement

TL;DR: The center weighted median (CWM) filter as discussed by the authors is a weighted median filter that gives more weight only to the central value of each window, which can preserve image details while suppressing additive white and/or impulsive-type noise.
Journal ArticleDOI

A theoretical analysis of the properties of median filters

TL;DR: In this article, the authors derived necessary and sufficient conditions for a signal to be invariant under a specific form of median filtering and proved that the form of successive median filtering of a signal (i.e., the filtered output is itself again filtered) eventually reduces the original signal to an invariant signal called a root signal.
Journal ArticleDOI

Morphological filters--Part I: Their set-theoretic analysis and relations to linear shift-invariant filters

TL;DR: The representation of classical linear filters in terms of morphological correlations, which involve supremum/infimum operations and additions, are introduced and demonstrate the power of mathematical morphology as a unifying approach to both linear and nonlinear signal-shaping strategies.
Journal Article

Stack filters

TL;DR: This investigation of the properties of stack filters produces several new, useful, and easily implemented filters, including two which are named asymmetric median filters.
Journal ArticleDOI

A generalization of median filtering using linear combinations of order statistics

TL;DR: In this paper, the authors consider a class of nonlinear filters whose output is given by a linear combination of the order statistics of the input sequence, and choose the coefficients in the linear combination to minimize the output MSE for several noise distributions.
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