Adaptive schemes for noise filtering and edge detection by use of local statistics

Sun, X.; Venetsanopoulos, A.N.

doi:10.1109/31.1700

Cited by 46 publications

(17 citation statements)

References 5 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…However, the 20%-trimmed t-test protects at most against a single outlier in case of the window widths considered here, and t-test on ranks had little robustness against outliers at all. Among tests based on local variabilities such as those based on quasi-ranges (Restrepo and Bovik, 1988, Sun and Venetsanopoulos, 1988, Kundu and Wu, 1989, Lee and Tantaratana, 1990, Sun, Gabbouj and Neuvo, 1994, only the empirical variance gave good power, but for the price of a strong increase of the test size already in case of a single outlier or a change of the variance. Similar problems were observed with linear hybrid edge detectors (Neuvo, Heinonen and Defee, 1987).…”

Section: Discussionmentioning

confidence: 99%

On the robust detection of edges in time series filtering

Fried

2007

Computational Statistics & Data Analysis

View full text Add to dashboard Cite

Abrupt shifts in the level of a time series represent important information and should be preserved in statistical signal extraction. We investigate rules for detecting level shifts that are resistant to outliers and which work with only a short time delay. The properties of robustified versions of the t-test for two independent samples and its non-parametric alternatives are elaborated under different types of noise. Trimmed t-tests, median comparisons, robustified rank and ANOVA tests based on robust scale estimators are compared.

show abstract

Section: Discussionmentioning

confidence: 99%

On the robust detection of edges in time series filtering

Fried

2007

Computational Statistics & Data Analysis

View full text Add to dashboard Cite

show abstract

“…As measures of the local signal activity [9] employed the local statistics, whereas [10] introduced a measure of the spatial detail, the visibility function. On the basis of these measures, several locally adaptive techniques have been proposed for image enhancement, which are efficient in preserving the detailed structure [11]- [13]. Local techniques, however, are not efficient in deconvolving the image from the degradation function.…”

Section: Introductionmentioning

confidence: 99%

Design of a new restoration algorithm based on the constrained mean-square-error criterion

Zervakis

Venetsanopoulos

1992

Multidim Syst Sign Process

View full text Add to dashboard Cite

Abstract. In this paper the joint optimization of different criteria is addressed, as a powerful means of incorporating a priori information in linear image-restoration algorithms. The constrained mean-square-error (CMSE) approach is introduced, which enables the incorporation of both spatial and spectral information regarding the peculiarities of the problem. Depending on the nature of the spatial information, this approach can be interpreted as either a regularized or an adaptive scheme. As a regularized scheme, it offers an alternative to conventional approaches, in which the ringing artifacts are efficiently suppressed by means of the regularizing operator. As an adaptive scheme, the CMSE approach offers the flexibility of applying either linear Wiener filtering or inverse faltering, depending on the local signal activity. Optimal techniques for the selection of the regularization parameter in both nonadaptive and adaptive cases are introduced. The capabilities of the CMSE approach as a regularized scheme and an adaptive scheme, are demonstrated through restoration examples.

show abstract

“…[11][12][13][14] These papers deal mainly with nonlinear approaches, which provide more satisfactory results than linear techniques. 15 Among them, some of the most popular filtering schemes are based on orderstatistics, 16 which can take different forms, from the median to the arithmetic mean.…”

Section: Introductionmentioning

confidence: 99%

Real-time adaptive method for noise filtering of a stream of thermographic line scans based on spatial overlapping and edge detection

Usamentiaga

García

Molleda

2008

J. Electron. Imaging

View full text Add to dashboard Cite

Abstract. Image filtering is a very important task in any imageprocessing system, since the output of the filtering constitutes the primary input to high-level vision, which then utilizes domain-specific knowledge to interpret and analyze the image contents. We propose a new approach to filtering the noise of a stream of thermographic line scans. The filter is designed to be applied in real time and is divided in two components: an intrascan filter and an interscan filter. The intrascan filter is based on spatial overlapping which occurs when using high acquisition rates. The interscan filter is based on edge detection and is able to adapt the way it produces the filtered output based on the detected edges. A procedure to automatically tune the parameters of the interscan filter is described and applied.The results of the proposed filters are compared to those obtained using the average and the median filter. In addition, the real-time performance of the proposed filters is analyzed by measuring the time taken by the tasks involved in their application.

show abstract

Adaptive schemes for noise filtering and edge detection by use of local statistics

Cited by 46 publications

References 5 publications

On the robust detection of edges in time series filtering

On the robust detection of edges in time series filtering

Design of a new restoration algorithm based on the constrained mean-square-error criterion

Real-time adaptive method for noise filtering of a stream of thermographic line scans based on spatial overlapping and edge detection

Contact Info

Product

Resources

About