基于边缘提取的非局部均值图像去噪
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摘要
图像去噪是图像处理和计算机视觉中的一个研究热点,其目的是从噪声图像中还原原始图像,且尽可能地保留更多的图像细节。非局部均值去噪算法(NLM)是近年来去噪效果比较出色的算法之一,但是传统的NLM算法在对边缘和纹理较丰富的图像进行去噪时,容易过平滑从而丢失很多细节信息。针对此问题,提出了一种基于边缘提取的NLM算法。该方法通过边缘提取,将图像边缘部分与平坦部分像素分开计算,减少不相似图像块之间的干扰,使得结构相似的邻域获得更大的权值,从而提高去噪效果的同时,保留更多的图像细节。实验结果表明:本文算法的去噪性能在主观评价和客观评价准则下都取得了较好的效果,去噪后的图像效果良好,且保留更多的细节。
Among image denoising is a hotspot in image processing and computer vision,with the aim of restoring the original image from the noise image and preserving as much detail as possible.Non-local mean denoising algorithm is one of the best algorithms for denoising in recent years.However,the traditional NLM algorithm is easy to smoothen the image when it is denoised at the edge and texture.Aiming at this problem,this paper proposes an NLM algorithm based on edge extraction.In this method,the edge of the image is separated from the flat part by the edge extraction,and the interference between the image blocks is reduced,so that the neighborhood of the similar structure can obtain more weight,so as to improve the denoising effect.More detail of the image.The experimental results show that the denoising performance of this algorithm has achieved better results under the subjective evaluation and objective evaluation criteria.The image after the denoising is good and retains more details.
Among image denoising is a hotspot in image processing and computer vision,with the aim of restoring the original image from the noise image and preserving as much detail as possible.Non-local mean denoising algorithm is one of the best algorithms for denoising in recent years.However,the traditional NLM algorithm is easy to smoothen the image when it is denoised at the edge and texture.Aiming at this problem,this paper proposes an NLM algorithm based on edge extraction.In this method,the edge of the image is separated from the flat part by the edge extraction,and the interference between the image blocks is reduced,so that the neighborhood of the similar structure can obtain more weight,so as to improve the denoising effect.More detail of the image.The experimental results show that the denoising performance of this algorithm has achieved better results under the subjective evaluation and objective evaluation criteria.The image after the denoising is good and retains more details.
引文
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[8]赵庆平,陈得宝,姜恩华,等.一种改进权重的非局部均值图像去噪算法[J].电子测量与仪器学报,2014,28(3):334-339.
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[11]VAN DE VILLE D,KOCHER M.Nonlocal means with dimensionality reduction and SURE-based parameter selection[J].IEEE Transactions on Image Processing,2011,20(9):2683-2690.
[12]王志明,张丽.自适应的快速非局部图像去噪算法[J].中国图象图形学报,2009,14(4):669-675.
[13]黄玲俐.一种改进权重的非局部均值图像去噪方法[J].计算机技术与发展,2016,26(6):16-19.
[14]吴涛,程小平.图像边缘检测[J].计算机光盘软件与应用,2013,16(13):46-48.
[15]王小俊,刘旭敏,关永.基于改进Canny算子的图像边缘检测算法[J].计算机工程,2012,38(14):196-198.
[16]冯伍,张俊兰,苗秋瑾.几种典型边缘检测算子的评估[J].电子设计工程,2011,19(4):131-133.
[2]BUADES A,COLL B,MOREL J M.Non-local means denoising[J].Image Processing On Line,2011,1:208-212.
[3]刘晓明,田雨,何徽,等.一种改进的非局部均值图像去噪算法[J].计算机工程,2012,38(4):199-201.
[4]刘卓亚.非局部均值图像去噪算法研究[J].电脑知识与技术:学术交流,2012,8(8):5448-5449.
[5]MANJN J V,COUPP,MART-BONMATL,et al.Adaptive non-local means denoising of MR images with spatially varying noise levels[J].Journal of Magnetic Resonance Imaging,2010,31(1):192-203.
[6]陈明举.基于统计特性的非局部均值去噪算法[J].液晶与显示,2014,29(3):450-454.
[7]蔡斌,刘卫,郑重,等.一种改进的非局部均值去噪算法[J].模式识别与人工智能,2016,29(1):1-10.
[8]赵庆平,陈得宝,姜恩华,等.一种改进权重的非局部均值图像去噪算法[J].电子测量与仪器学报,2014,28(3):334-339.
[9]PARKER A,INFANTES G,GRANT J,et al.Spot databases:efficient consistency checking and optimistic selection in probabilistic spatial databases[J].IEEE Transactions on Knowledge and Data Engineering,2009,21(1):92-107.
[10]WANG J,GE Y,HEUVELINK G B M,et al.Spatial sampling design for estimating regional GPP with spatial heterogeneities[J].IEEE Geoscience and Remote Sensing Letters,2014,11(2):539-543.
[11]VAN DE VILLE D,KOCHER M.Nonlocal means with dimensionality reduction and SURE-based parameter selection[J].IEEE Transactions on Image Processing,2011,20(9):2683-2690.
[12]王志明,张丽.自适应的快速非局部图像去噪算法[J].中国图象图形学报,2009,14(4):669-675.
[13]黄玲俐.一种改进权重的非局部均值图像去噪方法[J].计算机技术与发展,2016,26(6):16-19.
[14]吴涛,程小平.图像边缘检测[J].计算机光盘软件与应用,2013,16(13):46-48.
[15]王小俊,刘旭敏,关永.基于改进Canny算子的图像边缘检测算法[J].计算机工程,2012,38(14):196-198.
[16]冯伍,张俊兰,苗秋瑾.几种典型边缘检测算子的评估[J].电子设计工程,2011,19(4):131-133.