2008Unpublished venueRequires access

Noise filtering method for color images based on LDA and nonlinear diffusion

Woong Hee Kim, Thomas Sikora

Open publisher page 1 citations

Abstract

The purpose of noise filtering for images is to preserve features such as edge or corners in images, while reducing noise. Recent noise filtering algorithms based on diffusion equation shows the satisfactory results to some extent, if the noise is additive Gaussian noise. However, if the noise is not additive Gaussian noise, the filtering result is not satisfactory. In this paper, we propose a noise filtering method for color images based on LDA and nonlinear diffusion, which makes use of a common diffusion control. Experimental results with images degraded by additive Gaussian noise, salt and pepper noise, and multiplicative noise are presented.

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What this paper is about

The purpose of noise filtering for images is to preserve features such as edge or corners in images, while reducing noise. Recent noise filtering algorithms based on diffusion equation shows the satisfactory results to some extent, if the noise is additive Gaussian noise. However, if the noise is not additive Gaussian noise, the filtering result is not satisfactory. In this paper, we propose a noise filtering method for color images based on LDA and nonlinear diffusion, which makes use of a common diffusion control. Experimental results with images degraded by additive Gaussian noise, salt and pepper noise, and multiplicative noise are presented.

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Available abstract

The purpose of noise filtering for images is to preserve features such as edge or corners in images, while reducing noise. Recent noise filtering algorithms based on diffusion equation shows the satisfactory results to some extent, if the noise is additive Gaussian noise. However, if the noise is not additive Gaussian noise, the filtering result is not satisfactory. In this paper, we propose a noise filtering method for color images based on LDA and nonlinear diffusion, which makes use of a common diffusion control. Experimental results with images degraded by additive Gaussian noise, salt and pepper noise, and multiplicative noise are presented.

Key concepts: Salt-and-pepper noise, Value noise, Gaussian noise, Gradient noise, Noise (video), Multiplicative noise, Median filter, Noise measurement

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