2005Unpublished venueRequires access

Image Denoising Using Prethresholding Wiener Filtering

Rui Lü

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Abstract

This paper introduces the statistical errors occurring in Wiener filtering and presents possibility of improv- ing Wiener filtering performance in image denoising using prethresholding as a processing step. The results obtained by standard Wiener filtering, inverse filtering and prethresholding Wiener filtering in degenerated image processing show that in image restoration standard Wiener filtering is better than inverse filtering and prethresholding Wiener is much better than both standard Wiener filtering and inverse filtering.

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What this paper is about

This paper introduces the statistical errors occurring in Wiener filtering and presents possibility of improv- ing Wiener filtering performance in image denoising using prethresholding as a processing step. The results obtained by standard Wiener filtering, inverse filtering and prethresholding Wiener filtering in degenerated image processing show that in image restoration standard Wiener filtering is better than inverse filtering and prethresholding Wiener is much better than both standard Wiener filtering and inverse filtering.

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Available abstract

This paper introduces the statistical errors occurring in Wiener filtering and presents possibility of improv- ing Wiener filtering performance in image denoising using prethresholding as a processing step. The results obtained by standard Wiener filtering, inverse filtering and prethresholding Wiener filtering in degenerated image processing show that in image restoration standard Wiener filtering is better than inverse filtering and prethresholding Wiener is much better than both standard Wiener filtering and inverse filtering.

Key concepts: Wiener filter, Wiener deconvolution, Computer science, Noise reduction, Inverse, Wiener process, Image (mathematics), Algorithm

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