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An Improved Image Denoising Algorithm Based on Structural Similarity and Curvelet 被引量：1

An Improved Image Denoising Algorithm Based on Structural Similarity and Curvelet
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摘要 An image denoising method based on curvelet within the framework of non-local means(NLM) is proposed in this paper. We use Structural Similarity(SSIM) to compute the value of SSIM between the reference patch and its similar versions, and remove the dissimilar pixels. Besides, the curvelet is adopted to adjust the coefficients of these patches with low SSIM. Experiments show that the proposed method has the capacity to denoise effectively, improves the peak signal-to-noise ratio of the image, and keeps better visual result in edges information reservation as well. An image denoising method based on curvelet within the framework of non-local means（NLM） is proposed in this paper. We use Structural Similarity（SSIM） to compute the value of SSIM between the reference patch and its similar versions, and remove the dissimilar pixels. Besides, the curvelet is adopted to adjust the coefficients of these patches with low SSIM. Experiments show that the proposed method has the capacity to denoise effectively, improves the peak signal-to-noise ratio of the image, and keeps better visual result in edges information reservation as well.
作者 HE Ruo-nan YANG Wei-wei LI Mei
机构地区 Institute of Electronic and Information
出处 《科技信息》 2013年第1期60-60,38,共2页 Science & Technology Information
关键词 图像处理 SSIM NLM 计算机 Non local-mean （NLM） Image denoising Curvelet Structural similarity
分类号 TP391.41 [自动化与计算机技术—计算机应用技术]
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参考文献8

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同被引文献16

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