A Non Local Feature-Preserving Strategy for Image Denoising

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“A Non Local Feature-Preserving Strategy for Image Denoising,” Chinese Journal of Electronics, vol. 21, no. 4, pp. 651-656, 2012,

Citation:

“A Non Local Feature-Preserving Strategy for Image Denoising,” Chinese Journal of Electronics, vol. 21, no. 4, pp. 651-656, 2012,

“A Non Local Feature-Preserving Strategy for Image Denoising,” Chinese Journal of Electronics, vol. 21, no. 4, pp. 651-656, 2012,

Citation:

“A Non Local Feature-Preserving Strategy for Image Denoising,” Chinese Journal of Electronics, vol. 21, no. 4, pp. 651-656, 2012,

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A Non Local Feature-Preserving Strategy for Image Denoising

Received Date: 2011-09-01
Rev Recd Date: 2011-10-01
Publish Date: 2012-10-25

Abstract

Abstract

In this paper, we propose a variational image denoising model by exploiting an adaptive featurepreserving strategy which is derived from the Non-local means (NL-means) denoising approach. The commonly used NL-means filter is not optimal for noisy images containing small features of interest since image noise always makes it difficult to estimate the correct coefficients for averaging, leading to over-smoothing and other artifacts. We address this problem by a non-local detail preserving constraint, which is performed by adding two terms in the Total variation (TV) model. One is a non local patch based regularization term that controls the amount of denoising to preserve textures, small details, or global information, the other is a new data fidelity term, which forces the gradients of desired image being close to the smoothed normal. The Euler-Lagrange equation is used to solve the problem. Experimental results show that the proposed method can alleviate the over-smoothing effect and other artifacts, while preserving the fine details.

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References(16)

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