The WMMR filters: a class of robust edge enhancers

Longbotham, Harold G.; Eberly, David H.

doi:10.1109/78.212743

Cited by 30 publications

(22 citation statements)

References 17 publications

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“…At each coordinate , the maximum contribution to (7) is , and the maximum contribution to is . There is an interesting relationship between PICO regression or PICO estimation, and a robust class of image-enhancing order statistic filters, known as the weighted majority with minimum range (WMMR) filters [14]. The development of the WMMR filter was motivated by the fact that other impulserejecting nonlinear filters, such as the median filter, preserve undesirable monotonic degradation, such as blur, along image edges.…”

Section: A Pico Image Estimationmentioning

confidence: 99%

“…It has also been shown that repeated passes of a WMMR filter eventually produces a PICO root signal. (To achieve a PICO root signal, the WMMR weights must be nonnegative and sum to unity, with unequal first and last weights [14].) We may make the interpretation, then, that the PICO regression directly finds the fixed point of a WMMR filter.…”

Section: A Pico Image Estimationmentioning

confidence: 99%

“…The number of iterations needed to obtain this measure of uniform convergence at a temperature is given by [2] (12) where is the initial annealing temperature is the final temperature. Using the guidelines in [2] for the image estimation problem (13) and (14) where is the maximum energy change, and is the minimum (nonzero) energy change possible with one variable change. Because the minimum and maximum energy changes depend on the realization of , , and must be computed for each PIM and LIM.…”

Section: Iterative Solution Via Gdamentioning

confidence: 99%

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Nonlinear image estimation using piecewise and local image models

Acton

Bovik²

1998

IEEE Trans. on Image Process.

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Abstract-We introduce a new approach to image estimation based on a flexible constraint framework that encapsulates meaningful structural image assumptions. Piecewise image models (PIM's) and local image models (LIM's) are defined and utilized to estimate noise-corrupted images. PIM's and LIM's are defined by image sets obeying certain piecewise or local image properties, such as piecewise linearity, or local monotonicity. By optimizing local image characteristics imposed by the models, image estimates are produced with respect to the characteristic sets defined by the models. Thus, we propose a new general formulation for nonlinear set-theoretic image estimation. Detailed image estimation algorithms and examples are given using two PIM's: piecewise constant (PICO) and piecewise linear (PILI) models, and two LIM's: locally monotonic (LOMO) and locally convex/concave (LOCO) models. These models define properties that hold over local image neighborhoods, and the corresponding image estimates may be inexpensively computed by iterative optimization algorithms. Forcing the model constraints to hold at every image coordinate of the solution defines a nonlinear regression problem that is generally nonconvex and combinatorial. However, approximate solutions may be computed in reasonable time using the novel generalized deterministic annealing (GDA) optimization technique, which is particularly well suited for locally constrained problems of this type. Results are given for corrupted imagery with signal-to-noise ratio (SNR) as low as 2 dB, demonstrating high quality image estimation as measured by local feature integrity, and improvement in SNR.

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Section: A Pico Image Estimationmentioning

confidence: 99%

Section: A Pico Image Estimationmentioning

confidence: 99%

Section: Iterative Solution Via Gdamentioning

confidence: 99%

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Nonlinear image estimation using piecewise and local image models

Acton

Bovik²

1998

IEEE Trans. on Image Process.

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“…The hybrid function Pixel neighbor pattern analysis got the working and decision making logic of two edge enhancing filters comparison and selection (CS) and weighted majority of samples with minimum range (WMM) [24][25][26]. The function is defined as:…”

Section: Pnpa (Function P)mentioning

confidence: 99%

A Hybrid Method for Edge Continuity Based on Pixel Neighbors Pattern Analysis (PNPA) for Remote Sensing Satellite Images

Haider¹,

Malik²,

Raza³

et al. 2012

IJCNS

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Edge enhancement is derived from lack of accurate result from edge detection techniques. The image which is captured from long distances carries a lot of noise and blur which causes edge discontinuity. Although some novel algorithms which are based on cellular neural network, fuzzy enhancement and binary morphology have shown accuracy in order to obtain refined edge but still the problem of edge discontinuity arises. Eliminating discontinuity of edge a hybrid technique is proposed based on pixel neighbors pattern analysis PNPA. In the technique Canny operator for initial edge detection, PNPA operation for edge enhancement are performed for remote sensing satellite image successively. The visual and subjective evaluation shows that the proposed PNPA operation can effectively eliminate the influence of edge discontinuity which occurred due to noise and blurr in original captured image, as comparing to existing edge segmenting processes.

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“…The response of one-and two-dimensional filters to edges has been a topic of much research [1][2][3]. The edge response of nonlinear filters such as the median filter is one of the primary reasons such filters have become important in signal and image processing applications.…”

Section: Introductionmentioning

confidence: 99%

New method for quantifying the response of filters at corners

Schulze

Pearce

1994

SPIE Proceedings

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Although the edge response of nonlinear filters has been the topic of much research, the behavior of many filters at two-dimensional structures such as sharp corners is not as well understood. We introduce a new technique that defines the response of a two-dimensional filter at corners of any angle. The response measure may be expressed either as fractional preservation or as an attenuation in decibels. Plotting the response measure in polar form illustrates the corner response of a filter both intuitively and quantitatively. The corner response may be computed for a particular size and shape of filter window on a discrete lattice, or may be determined in continuous space for a filter with a specific window shape. The continuous space response measure gives the response of the filter to corners in general, and usually corresponds closely with that determined for the filter on a discrete lattice. The corner responses of several widely used filters (median, averaging, and morphological) are compared.

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The WMMR filters: a class of robust edge enhancers

Cited by 30 publications

References 17 publications

Nonlinear image estimation using piecewise and local image models

Nonlinear image estimation using piecewise and local image models

A Hybrid Method for Edge Continuity Based on Pixel Neighbors Pattern Analysis (PNPA) for Remote Sensing Satellite Images

New method for quantifying the response of filters at corners

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