2002Unpublished venueRequires access

Robust B-spline image smoothing

Marta Karczewicz, Moncef Gabbouj, Jaakko T. Astola

Open publisher page 5 citations

Abstract

In this work we present a new approach to two-dimensional robust spline smoothing. The proposed method is based on M-estimator algorithms but unlike in other M-estimator based image processing algorithms it takes into consideration spatial relations between picture elements. The contribution of the sample to the model depends not only on the current residual of that sample, but also on the neighboring residuals. The smoothing parameter (/spl lambda/) is estimated separately for each processing window and it adapts to the local structure of the image. In order to test the proposed algorithm we apply it to image filtering problem. We show that the filter based on our algorithm has excellent detail preserving properties while suppressing additive Gaussian and impulsive noise very efficiently.>

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What this paper is about

In this work we present a new approach to two-dimensional robust spline smoothing. The proposed method is based on M-estimator algorithms but unlike in other M-estimator based image processing algorithms it takes into consideration spatial relations between picture elements. The contribution of the sample to the model depends not only on the current residual of that sample, but also on the neighboring residuals. The smoothing parameter (/spl lambda/) is estimated separately for each processing window and it adapts to the local structure of the image. In order to test the proposed algorithm we apply it to image filtering problem. We show that the filter based on our algorithm has excellent detail preserving properties while suppressing additive Gaussian and impulsive noise very efficiently.>

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OpenAlex reports 5 citations for this work. Citation counts describe recorded attention and do not establish research quality.

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Available abstract

In this work we present a new approach to two-dimensional robust spline smoothing. The proposed method is based on M-estimator algorithms but unlike in other M-estimator based image processing algorithms it takes into consideration spatial relations between picture elements. The contribution of the sample to the model depends not only on the current residual of that sample, but also on the neighboring residuals. The smoothing parameter (/spl lambda/) is estimated separately for each processing window and it adapts to the local structure of the image. In order to test the proposed algorithm we apply it to image filtering problem. We show that the filter based on our algorithm has excellent detail preserving properties while suppressing additive Gaussian and impulsive noise very efficiently.>

Key concepts: Smoothing, Estimator, Gaussian blur, Algorithm, Spline (mechanical), Computer science, Smoothing spline, Image (mathematics)

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