2011•Unpublished venueRequires access

A model of image denoising based on partial differential equations

Jing Liu, Fei Gao, Zuozhou Li

Open publisher page 9 citations

Abstract

Based on the isotropic diffusion denoising model and the total variation (TV) denoising model, an image denoising model, which is constructed by the partial differential equations assigned a weight function is proposed. This model can improve "staircase effect" and "block effect" compared to the isotropic diffusion denoising model and the total variation (TV) denoising model, respectively. Numerical experiments show that the model is able to remove noise effectively while preserving texture features and edge information well, so that the quality of the denoised image is enhanced.

About this research paper

What this paper is about

Based on the isotropic diffusion denoising model and the total variation (TV) denoising model, an image denoising model, which is constructed by the partial differential equations assigned a weight function is proposed. This model can improve "staircase effect" and "block effect" compared to the isotropic diffusion denoising model and the total variation (TV) denoising model, respectively. Numerical experiments show that the model is able to remove noise effectively while preserving texture features and edge information well, so that the quality of the denoised image is enhanced.

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OpenAlex reports 9 citations for this work. Citation counts describe recorded attention and do not establish research quality.

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Available abstract

Based on the isotropic diffusion denoising model and the total variation (TV) denoising model, an image denoising model, which is constructed by the partial differential equations assigned a weight function is proposed. This model can improve "staircase effect" and "block effect" compared to the isotropic diffusion denoising model and the total variation (TV) denoising model, respectively. Numerical experiments show that the model is able to remove noise effectively while preserving texture features and edge information well, so that the quality of the denoised image is enhanced.

Key concepts: Noise reduction, Image denoising, Partial differential equation, Anisotropic diffusion, Total variation denoising, Noise (video), Isotropy, Diffusion

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